esa-sceva/satcom-chunk-collection huggingface dataset:
# Distributed Ledger Technologies and Their Applications: A Review
## 2 Distributed Ledger Technology
### Tangle
#### 2.2.1. Tangle Life Cycle  
The genesis transaction was the first transaction in the IOTA tangle that created all IOTA coins and distributed them to the initial investors [85]. For Blockchain, the issue was the size of the block and the energy required to validate the block, which is a bottleneck for Blockchain technologies; therefore, in IOTA there are no blocks, and transactions are recorded instead. Once the genesis node is recorded, other transactions verify the genesis transaction, become unconfirmed transactions, and remain in a pool of transactions. When a transaction needs to occur, the provider of that transaction will choose two unconfirmed transactions from the pool with a tip selection algorithm and validate them and check double spending on both, linking their transaction to those unconfirmed transactions. The graph will be cut if any transactions deemed invalid are linked to those invalid transactions [82]. Transactions will be confirmed when they reach a cumulative score of approvals from new transactions. In IOTA, there are entities called coordinators that provide security and scalability. IOTA foundation controls coordinators, which confirm the network and find invalid transactions. The problem with coordinators is their centrality over the network and their vulnerability to single failure node attacks [86].
# An Efficient Ultra-Tight GPS/RISS Integrated System for Challenging Navigation Environments
## 3 The Proposed Method  
The proposed ultra-tight GPS/INS integration system uses a centralized Kalman filter. The filter is updated from the tracking loops discriminators and its states are used to predict the pseudoranges and pseudorange-rates between measurement epochs. The estimates of the filter residuals (in accordance with the line of sight (LOS) geometry) are passed to correct the filter's predicted pseudoranges and pseudorange-rates. The filter also applies its estimates of the IMU error states to correct the IMU data. In the ultra-tightly coupled GPS/INS integration, the signals from the receiver correlator, I and Q components, form the measurements of the filters. These I and Q measurements are integrated with the position, velocity and attitude of the INS. The code phase and Doppler shift calculated from the INS and satellite ephemeris are used to control both the code numerically controlled oscillator (NCO) and carrier NCO. The number of measurements is twice of the number of satellites being used for navigation.  
In the ultra-tightly coupled GPS/INS integration, the measurement errors are obtained directly from the tracking loops not at the navigation filter level. In fact, the pseudorange errors and pseudorange rate errors are the scaled code phase errors and frequency errors, respectively. Every time a full cycle of tracking/navigation is completed, the carrier frequency and code frequency are estimated and fed back into the tracking loops. A high-level diagram of the proposed system is depicted in Figure 5.
# Parameter Conversion between Controlled Pass-By Method and Alternative Close Proximity Method
## 3 Results and Correlation Analysis  
The tire noises recorded between CPB and the A-CPX method after linear regression correction are compared in Table 3. We found that the tire/pavement noise recorded by the A-CPX method on the cement road was about 32 dB higher than that of the CPB (Table 3). However, the ranking of the tire assigned by the two experimental methods was the same.  
To study the feasibility of using the A-CPX experiment results to predict the CPB tire noise value, we first examined the correlation between the experimental results of the two methods. The correlation coefficient between the noise experiment results of CPB and A-CPX is shown in Figure 3, which was 0.973. Therefore, the noise experiment results of the CPB method can be predicted using the noise experiment results obtained using the A-CPX method.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Tire Type**} & **CPB Experimented** & **A-CPX Experimented** & \multirow{2}{*}{**Difference Value/dB**} \\  & **Value/dB** & **Value/dB** & \\ \hline a & 76.1 & 108.1 & 32.0 \\ b & 78.0 & 110.2 & 32.2 \\ c & 79.4 & 111.2 & 31.8 \\ d & 77.1 & 108.9 & 31.8 \\ e & 78.0 & 110.1 & 32.1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of tire noise between CPB and A-CPX methods.  
Figure 2: Probe location and equipment configuration of the A-CPX method.  
Analysis of Parameter Conversion Relationship between the Noise Experiment Results of CPB and A-CPX  
The CPB method is used to determine four tires' pass-by noise in a semi-free field. The A-CPX method is used to determine the noise of a single tire. To determine the quantitative relationship between the two methods, the attenuation relationship in the propagation path must be determined, including the attenuation of the sound pressure level at each frequency and the correction of the number of sound sources and other factors [22].  
Because the A-CPX method is a near-field experiment and the CPB method is a far-field experiment, the process of predicting far-field noise from near-field noise is essentially an extrapolation process [12]. The physical configuration of near-field noise extrapolated to far-field noise is shown in Figure 4[12]. To discuss the transmission relationship between near-field noise and far-field noise and to simplify the theoretical derivation of the extrapolation process, we assumed that the statistical characteristics of the four tires' sources were consistent and all of them were incoherent noise. We also assumed that the contributions of the front and rear tires of the vehicle to the far-field sound pressure measurement were the same.
## 6 V-DPAP Solution Methods
### Round-Robin Allocations (p-rr and n-rr)  
One fast approach to the fair allocation of indivisible goods is _round-robin_. This consists in making each agent choose in turn (in a predefined fixed order) one item (depending on the preferences) until there is no more item to allocate. It is polynomial in the number of agents and items. In our case, one may consider two kinds of items to allocate: paths (denoted p-rr) or nodes (denoted n-rr). In the case of paths, each agent selects at its turn its best feasible path, given the already allocated nodes (to prevent conflicts). This process operates similarly to greedy, but alternates between users to balance utilities. In the case of nodes, each agent incrementally builds the path associated with each of its graphs, by choosing in turn a next best feasible node until either the sink is reached or there is no more feasible nodes to choose (dead-end path). In the latter case, the agent is allocated the 0-utility source-to-sink path and loses the previously chosen nodes. In both approaches, constraints (1)-(6) are met since all the paths considered are feasible. Note that if there are no ties for the best path chosen by an agent at its turn, then p-rr results in a Nash equilibrium. This is not true for n-rr, since some nodes left by some agents reaching a dead-end may have prevented some other agents from finding a better solution. To overcome this difficulty, it is possible to increase the possible partial satisfaction schemes for a request, e.g., by adding arcs with a null utility from any node \(v\) to the sink node.  
**Example 11**.: _In Figure 3, if request a begins the path round-robin allocation, \(\pi_{\mathbf{p}\text{-}\boldsymbol{r}}\) for the example in Figure 3 is equivalent to \(\pi_{\mathbf{greedy}}\), since a chooses \(\{s_{\mathbf{a}},a_{3},a_{2},t_{\mathbf{a}}\}\) and then b chooses \(\{s_{\mathbf{b}},b_{1},b_{4},t_{\mathbf{b}}\}\). If request b begins, then b chooses \(\{s_{\mathbf{b}},b_{1},b_{2},t_{\mathbf{b}}\}\) and then the only possible path for a is \(\{s_{\mathbf{a}},t_{\mathbf{a}}\}\), meaning that agent A receives a null utility._  
_If request a begins, the node round-robin allocation \(\pi_{\mathbf{n}\text{-}\boldsymbol{r}}\) is equivalent to \(\pi_{\mathbf{greedy}}\) because a first chooses a\({}_{3}\), then b chooses b\({}_{1}\) (only feasible option), then a chooses a\({}_{2}\), and finally b chooses b\({}_{4}\) (only feasible option). However, if request b begins, b first chooses b\({}_{1}\), then a chooses a\({}_{3}\), then b chooses b\({}_{2}\), and finally a reaches a dead-end, since the selection of b\({}_{2}\) implies that a\({}_{2}\) cannot be selected._
## 2 Modern Physical Layer  
The physical layer defines the mechanism for transmitting bits over a physical link channel connecting network nodes. The transmitter converts bit streams into a physical signal that is propagated through the physical layer. On the other side, the receiver should be able to reverse the process and provide the original bit stream to the upper communication layers.  
The main issues tackled by the physical layer in a UWSN and discussed throughout this section are as follows: interface to physical transmission media, modulation, equalization filtering, efficient carrier sense and collision detection (used by the MAC layer); additionally, other essential services include bit rate, bit synchronization and forward error correction.  
The goal of physical layer design is to provide robust and reliable communications with the highest spectrum efficiency, which is the ratio of the communication bit rate to the channel bandwidth. Additionally, because UWSN nodes are usually autonomous and powered by batteries, power saving is also a key physical layer issue.
# A Refined Shape Sensing Method for Skin Antenna Structure Based on Inverse Finite Element Method
## 2 Inverse Finite Element Formulation
When the element matrix equations are established, the global deformation field can be performed explicitly according to the transformation relationship from the local to global coordinates. \[KU=F \tag{12}\]  
As \(K\) includes the rigid body motion mode of the discretized structure, therefore, it is a singular matrix. By combining problem-specific displacement boundary conditions, the resulting system of equations can be reduced from Equation (12)  
\[K_{a}U_{a}=F_{a} \tag{13}\]  
where, \(K_{a}\) is a \(6\text{m}\times 6\text{m}\) square matrix and invertible [13; 24], \(F_{a}\) is a \(6m\times 1\) matrix, and \(U_{a}\) is a \(6m\times 1\) vector. The solution of Equation (12) is very efficient, because the matrix \(K_{a}\) remains unchanged for a determined configuration of sensors in the process of constant deformation. Moreover, \(F_{a}\) needs to be updated during any deformation cycle in real time. Finally, the unknown vector \(U_{a}\) reflects the structural state as well at any time. According to general space interpolation methods, all the methods satisfy the estimation formula [25]  
\[u(x,y)=\sum_{i=1}^{m}w_{i}(x,y)u_{i} \tag{14}\]  
where \(u(x,y)\) is the response value at the interpolated point in the structure, \(w_{i}\) indicates weighting function associated to the \(i\)-th sample point corresponding to the value \(u_{i}\), and m represents the numbers of sampling points. However, the inverse elements divided are evidently bigger than that fem, and the full field deformation obtained by Equation (12) has a distinct difference. The approximation approach is not suited for larger inverse elements. Based on the idea of constructing shape function shown [26], one can adapt a refined interpolation function that the nodes may be in the element rather than only in the boundary. The method employs the properties of shape function to improve the precision for a specific problem. The shape function can be described in the discrete vector  
\[N_{i}=[N_{i}(x_{1},y_{1}),N_{i}(x_{2},y_{2}),\ldots\ldots,N_{i}(x_{n},y_{n})] \tag{15}\]  
where \(N_{i}\) is \(i-th\) node shape function, \((x_{n},y_{n})\) is the coordinate in the element, \(n\) indicates the number of selected positions, and the arbitrarily point of the structure is expressed according to the interpolation definition  
\[u(x,y)=N_{1}u_{1}+N_{2}u_{2}+\ldots+N_{i}u_{i} \tag{16}\]  
The interpolation methods satisfy the four properties on shape function, and the geometric characteristics of the structure are described in some sense, overcoming the deficiency of little physical significances.
# Discerning Reality through Haze: An Image Dehazing Network Based on Multi-Feature Fusion
## 4 Experiment and Result Analysis  
### Experimental Environment  
In order to comprehensively evaluate the performance and effects of the algorithm, it is necessary to use datasets for training, testing, and validation. The commonly used image dehazing datasets include NYU2 [45], D-HAZY [46], and RESIDE [47]. We chose RESIDE for training, testing, comparison, and ablation experiments. This dataset consists of five subsets, including multiple synthetic and real foggy images. We selected 13,990 indoor and 13,990 outdoor images from it to ensure that the algorithm could handle images under various scenarios. To further validate the effect of the algorithm, we also selected 500 indoor and 500 outdoor images from the SOTS subset of the RESIDE dataset as the validation set. To evaluate the performance of the algorithm in real applications, we chose the real dataset from RESIDE for validation.  
To ensure the fairness of the experimental results, all algorithms were trained in the PYTORCH framework and on two GTX 1080 ti graphics cards. During the training process, we used the Adam optimizer for 100 epochs of training, with 5000 samples trained per epoch. The initial learning rate was set to 0.0001, and the learning rate was adjusted using the cosine annealing strategy until the learning rate dropped to zero. Finally, we chose the best-performing result during training as the final result of the experiment.
# A Case Study of Floating Offshore Super-Long Steel Pipeline Combing with Field Monitoring
## 2 Project Characteristics  
### Project Profiles  
Raw water pipeline project locates in Tongan Xiamen (Pile No. C2 + 187.77\(\sim\)C3 + 565.91). The length of pipeline 1 is 595 m in the straight pipeline and 120m in the oblique pipeline section section, totally 715 m; the length of pipeline 2 is 370 m in the straight pipeline section and 290 m in the oblique pipeline, totally 660 m. The whole pipeline is 1375 m long, 1600 mm in diameter, about 1087 t in weight, 20 mm in the wall thickness, and coated with 3 PE anticorrosive coating on the surface. The whole pipeline is similar to the inverted rainbow pipeline structure. Both of the two pipelines are relatively long, so it is difficult to arrange the position of tugboats and towing force during floating transportation. The reasonable arrangement of tugboats can make the pipelines bear even force in the floating transportation process and avoid the local deformation of the pipelines. The floating pipeline section operates in a cross-flow direction from the downstream direction, with a turning angle of about 90\({}^{\circ}\), and the detailed floating routes of the two pipelines are illustrated in Figure 1. The vertical distance of the floating pipeline is 800 m and the route is 1.15 km. Due to the influence of sea surface width and the Tong'an bridge, it is difficult to float the whole pipeline at a time. Therefore, the pipeline 1 was floated to the sinking position firstly, then the pipeline 2 was floated, the two pipelines were welded at the sinking position in the sea. Finally, the whole pipeline was sunk at once. The floating transportation process is shown in Figure 2.
# Effectiveness of Augmented Reality in Stroke Rehabilitation: A Meta-Analysis
## 3 Results and Discussion  
Figure 2 shows the systematic review process. We found 556 records using keywords (Augmented Reality OR AR) AND (stroke OR Hemiplegia) AND (rehabilitation) AND (Upper Limb OR Upper Extremity OR Lower Limb OR Lower Extremity OR Gait and  
Figure 1: Population, Intervention, Comparison, Outcome (PICO) Question and the main variables included in the systematic review and meta-analysis.  
Balance) in the five electronic databases as described in Table 1. A total of 22 articles were removed because of duplication, and 534 articles were excluded based on our criteria. Finally, 13 articles were selected through full-text checking. Among the final 13 articles, there were 9 observational investigations for upper limb rehabilitation and 4 investigations for balance and gait.
## 4 Discussion
### Comparison with Real Lighting Scenario  
Our findings were validated by producing linear luminaires modeled on the L2-AL6 design and using them to illuminate the ceilings of several office rooms. While the specifics of the luminaire design are not covered in this work, the simulation followed the same procedure described for the L2-AL6. Notably, the real luminaires used LED packages and power supply that delivered a higher luminous flux (1700 lm), but this did not affect the OUF and U\({}_{0}\) generated by the luminaires. For comparison, a typical office space measuring 6 \(\times\) 3 \(\times\) 3.8 m (length \(\times\) width \(\times\) height), with reflectance values of 85% for the ceiling, 70% for the walls, and 50% for the floor was selected. Ten L2-AL6-based luminaires were mounted 0.225 m from the ceiling, maintaining the previously calculated \(H/W\) (0.3/4) ratio, and were positioned on opposite sides along the ceiling.  
The comparison between the simulated and actual ceiling luminance is presented in Figure 10a-d. Figure 10a displays a simulated ceiling image with calculated luminance values based on the total illuminance figures detailed in Figure 10b. The values in parentheses indicate the luminance corresponding to the illuminance at each ceiling position, averaging 234 cd/m\({}^{2}\). Figure 10c features a real ceiling image with measured luminance values, captured using a Konica-Minolta LS-150 Luminance Meter, arranged in a grid that mirrors the simulation grid. The minimal differences in average ceiling luminance (234 vs. 232 cd/m\({}^{2}\)) and total uniformity \(U_{0}^{total}\) values (0.85 vs. 0.82) validate our model and design approach, confirming their applicability in LED luminaire development.
# A Microservice-Based Smart Agriculture System to Detect Animal Intrusion at the Edge
## 6 Evaluation
#### 5.1.2 Sensor Placement  
To detect animals in a 25 by 25 m field, we used PIR sensors. As described in Section 4.3, the layouts of the sensors were accurately deployed. The sensors were fixed to a strip and placed around the perimeter of the field to ensure complete coverage. We used an Arduino ATMega2560 along with a LoRa hat using LoRa SX1276 IC powered by a lithium-ion battery to send data to the Gateway for processing and decision-making.  
We changed different speeds, directions, and trajectories to simulate 18 different movements (M1-M18 in Figure 8) of an animal (cow) to evaluate the accuracy and effectiveness of our algorithm. For each of these movements, our system sensed and transmitted PIR sensor values to the edge server for multiple locations depending on which sensors detected movements. For example, Table 2 shows the location values received for Movement M2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Component (Manufacturer, City, Country)** & **Specifications** \\ \hline Edge Server (Foxconn, Zhengzhou, China) & 3.1 GHz Dual-Core Intel Core i5, 8 GB RAM, 256 GB Disk \\ \hline LoRa SX1276 (Dragino, Shenzhen, China) & Frequency: 868 MHz, Bandwidth: 7.8 kHz to 500 kHz, Data Rate: 18 bps to 27.5 kbps \\ \hline PG1302 Concentrator (Dragino, Shenzhen, China) & Tx power up to 27 dBm, Rx sensitivity down to -139 dBm@SF12, BW 125 kHz \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 7: Experimental setup architecture.  
These location data collected from 18 different movements formed the ground truth for our evaluation.
## 3 Results and Discussion
### Characterization of nZVISLP
#### 3.1.6 X-Ray Diffraction (XRD)  
The synthesized adsorbent was analyzed through XRD to determine its crystallinity, and the pattern is shown in Figure S3 (Supplementary Materials). The absence of any distinct peak confirmed  
Figure 4: (**a**) Particle size and (**b**) zeta potential of nZVISLP.  
that the green-synthesized iron nanoparticles were amorphous in nature. This result indicates the amorphous nature of nZVI, and the XRD results were very similar to experimental studies reported by [PERSON] et al. [34] and [PERSON] et al. [15], who also reported the amorphous nature of iron nanoparticles synthesized through a green route. The peak at 26 34.8\({}^{\circ}\) was attributed to iron oxide, and the peaks at 20 45.3\({}^{\circ}\) and 64.3\({}^{\circ}\) represented the zero-valent iron; these values are in close agreement with the reported XRD spectrum for zero-valent iron by [PERSON] et al. [35].
## 2 Improve Based on Hss
### YOLO11 Improved Based on Hss
#### 2.2.3 Introduction of Inner-SloU  
This model uses the Inner-SloU loss function instead of CIoU and GIoU, aiming to enhance the detection of small targets in complex environments.  
IoU is commonly used as a standard metric to evaluate object detection models. It quantifies the overlap between the predicted bounding box and the ground truth box by calculating the ratio of the area of their intersection to the area of their union.  
In object detection tasks, small targets are often difficult to detect accurately due to their limited number of pixels. Traditional IoU loss functions may fail to provide sufficient gradient information for optimizing bounding box regression when handling small targets. Inner-SloU accelerates the regression process by introducing auxiliary bounding boxes, making it especially suitable for small object detection. By adjusting the scale of these auxiliary bounding boxes, Inner-SloU provides more gradient information for small targets, thereby improving detection performance. The introduction of auxiliary bounding boxes and dynamic scaling factors allows the model to adapt more flexibly to different detection tasks and datasets.  
As shown in Figure 5, \(w^{e}\) and \(h^{e}\) represent the width and height of the minimum enclosing rectangle that contains both the predicted and ground truth bounding boxes, respectively, and are used to calculate the Euclidean distance between two specified points.  
In the experiments conducted under dim environments, Inner-SloU demonstrated excellent performance across various detection tasks, showing strong generalization ability. Typically, in training object detection models, the convergence speed directly affects both training efficiency and final performance. Therefore, Inner-SloU focuses on the core part of the bounding box rather than the whole, providing a more precise evaluation of the overlapping region. This accuracy helps to speed up the convergence process of the model,  
Figure 5: Schematic diagram of EIoU losses.  
allowing it to achieve better performance in a shorter period of time. The process is shown in Figure 6.  
\(x_{c}^{gt}\), \(x_{c}^{gt}\) are the center points of the Target Box and Inner Target Box, respectively, while \(x_{c},y_{c}\) are the center points of the Anchor and Inner Anchor, respectively.
## 3 Materials and Methods
### Chemicals and Reagents  
Polymer analytical standards for GPC/SEC were acquired for polypropylene (PP, Mw 1175 Da; Mn 1120 Da; PDI [Mw/Mn] 1.04) from American Polymer Standards Corporation (Mentor, OH, USA). For polyisoprene (PI, Mw 1000 Da; Mn 854 Da; PDI [Mw/Mn] 1.17) and polybutadiene (PBD, Mw 1000 Da; Mn 925 Da; PDI [Mw/Mn] 1.08) STD KIT (from Waters Cromatografia (Cerdanyola del Valles, Barcelona, Spain), and polyethylene (PE) and polystyrene (PS) (Mw 1220 Da; Mn 1120 Da; PDI [Mw/Mn] 1.09) provided by Polymer Standard Service GmbH (PSS, Mainz, Germany).  
Nitric acid (65% purity), NaCl (99% purity), and KOH (99.99% purity) were provided by Sigma-Aldrich (Steinheim, Germany).  
Methanol and water (HPLC grade) were both purchased from LiChrosolv (Darmstadt, Germany). Toluene (HPLC grade) was supplied by SupraSolv (Darmstadt, Germany).  
Glass microfiber filters of 0.70 \(\upmu\)m pore size were acquired from Whatman (GF/F, Whatman TM, Maidstone, UK).