esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion  
### Results  
The traditional 3D point cloud segmentation algorithm is primarily based on strict artificial design characteristics formulated from the geometric constraints and statistical rules of the point cloud. For example, the Euclidean clustering algorithm calculates the Euclidean distance between point clouds, and then the point with the smallest distance is divided into a category until the Euclidean distance between any two clusters is greater than a pre-set threshold [45]. The RANSAC algorithm is a model-based method of point cloud segmentation; however, the algorithm must manually define or select a model, usually a plane, sphere, or other geometry that can be represented by algebraic formulas [46]. The region growth algorithm is a classic point cloud segmentation method that measures the similarity between point clouds by combining features between N points or N area units and merging them [47].  
In this study, four different point cloud segmentation algorithms, namely Euclidean clustering-based segmentation, RANSAC-based segmentation, traditional region growing-based segmentation, and the improved region growing algorithm, were compared in two typical highway scenarios. The aim was to effectively validate the efficiency, accuracy, and robustness of the proposed algorithm in automatically extracting road surfaces from MLS data.  
By comparing the road surface extraction results obtained from these four segmentation algorithms, this study aimed to assess the performance of the proposed method and its ability to handle unstructured road surfaces in MLS point cloud data.
# GNSS Spoofing Detection and Mitigation Based on Maximum Likelihood Estimation
## 6 Performance Evaluation with TEXBAT
### Scenario 2 of TEXBAT
Here, since the total power of the spoofing signals is higher, the spoofing signals will be constructed and canceled in the estimation-cancellation procedure. Consequently, \(\hat{\rho}_{2}\) corresponds to the authentic PVT results. Figure 11b shows the biases of the user clock offsets in Scenario 2. The spoofed, unspoofed and recovered PVT results are indicated by blue, green and red traces, respectively. The recovered results are only shown when \(r_{2}\) is larger than four. Since the spoofing signals only falsify the timing results, only the biases of the user clock offsets are shown. Figure 11b shows that the red trace (recovered result) is close to the green trace (unspoofed response) after the time error induced by spoofing signals is more than 0.2 \(\upmu\)s (about 1/5 C/A code chip length), which demonstrates that the proposed method can recover the navigation solution even when the code phase differences between the spoofing and authentic signals are much smaller than one code chip.
## 5 Results
### Peak Throughput
#### 5.2.2 Residential Area  
Experimental throughput results for TCP and UDP traffic were obtained in an outdoor residential environment with LoS, as detailed in Table 3. As shown in Figure 9, the highest throughput was recorded in the 5 GHz and 6 GHz bands. Specifically, WiFi\({}^{6E}\)_6160 and WiFi\({}^{7}\)_5160 delivered the best performance at short distances, achieving approximately 1720 Mbps and 1700 Mbps for TCP traffic. At longer distances, WiFi\({}^{6}\)_5160 outperformed other technologies, reaching up to 1450 Mbps. This superior performance can be attributed to minimal interference in the 5 GHz and 6 GHz bands, combined with the high bandwidth (BW = 160 MHz) enabled by these configurations, supported by Equations (2) and (4).  
Figure 8: Peak throughput for TCP and UDP traffic measured for each of the Wi-Fi technologies in a laboratory environment.  
Figure 9: Peak throughput for TCP and UDP traffic measured at 5 m, 15 m, and 60 m in a residential environment for each of the Wi-Fi technologies.  
Additionally, it is observed that Wi-Fi 6 consistently outperforms WiFi5 and WiFi4 technologies, except at CD = 60 m, where a decrease in performance is evident. At this distance, the efficiency of Wi-Fi 5 and Wi-Fi 4 remains relatively stable, with low jitter and no packet loss, likely due to their narrower channel bandwidths and lower spectral demands, which reduce sensitivity to external factors. In contrast, Wi-Fi 6 configurations exhibit increased jitter and packet loss, suggesting that at longer distances, the use of wider channels (80 MHz and 160 MHz) may lead to greater susceptibility to channel fading, multipath effects, and overall spectral inefficiencies, ultimately impacting performance.  
In the case of UDP traffic, the WiFi6E -68 configuration outperforms WiFi6E -6160, suggesting that the narrower bandwidth (80 MHz) may offer better performance in specific environments with less interference. However, none of the other technologies evaluated exceed the performance levels achieved by WiFi6E -680 and WiFi6E -6160. This highlights that the selection of the frequency band and available bandwidth are the most influential factors in network performance across all Wi-Fi standards.  
For Wi-Fi 7, the performance shows some inconsistency in comparison with Wi-Fi 6. At shorter distances (C.D. = 5 m and 15 m), Wi-Fi 7 achieves comparable performance, with slightly better throughput and lower jitter in some cases. However, at longer distances (CD = 60 m), the performance of Wi-Fi 7 fluctuates, with higher jitter and some packet loss observed, suggesting that current Wi-Fi 7 devices and drivers may still be undergoing optimisation. These results highlight that while Wi-Fi 7 offers theoretical advantages in terms of speed and capacity, its current performance might be hindered by early-stage driver and firmware implementations.
# Evaluation of the Barriers to Maintenance 4.0 for the Textile Industry via Pythagorean Fuzzy SWARA
## 3 Materials and Methods
### Barriers to Maintenance 4.0
#### 3.1.5 Financial Main Criteria  
* High initial capital investment: The main criterion that prevents the adoption of Maintenance 4.0 is the high initial capital investment. Integrating advanced technologies, such as sensors, IoT devices, big data infrastructures, artificial intelligence-based software, and digital twins, required for Maintenance 4.0, are activities with high licensing, training, and operating costs [1; 43].
* Uncertainty regarding economic returns: Uncertainty around the return rate is a significant barrier for Maintenance 4.0 applications. The advantages offered by Maintenance 4.0 emerge in the long term. In addition, the performance of the system depends entirely on the correct collection and analysis of data. Uncertainty in the realization of the economic benefits of Maintenance 4.0 poses a barrier for businesses [1; 43].
* Lack of governmental and institutional support: Lack of government and institutional support is a systemic barrier to the spread of Maintenance 4.0 applications. Because the Maintenance 4.0 technological transformation process is quite costly, it requires multi-dimensional external support mechanisms such as financial incentives, infrastructure investments, educational support, and the establishment of standards [13; 46].
# Comparative Analysis of Major Machine-Learning-Based Path Loss Models for Enclosed Indoor Channels
## 3 Data Preparation and Models Setup  
This section provides information on the preparation of the raw data for ML-based path loss prediction. Moreover, the hyperparameters tuning method selected to achieve the best models' performance is presented in this section. Furthermore, the justification of the models' stability is discussed. Finally, this section presents the evaluation metrics adopted to measure and compare the models' performance.
## 3 Concepts and Methods
### Subgraph Association and Seed Mapping
Further, the pseudocode for the seed mapping algorithm is as follows (Algorithm 3):  
```
1.for i in range(\(n\))do
2. weights[i] = 0
3.endfor
4. // count the total weights corresponding to each subtask on its trace
5.for func in trace do
6. weights[\(MST\)[func]['subtask']] += func.weight
7.endfor
8. // arrange according to weights
9. no = getMaxIndex(weights)
10. arrangeSeed(seed, \(S_{no}\))
11. // if this seed has larger weights than the seed in remainSeeds then update it
12.if s has larger weights than remainSeeds[a][b] do
13. insert(s, remainSeeds[a], b)
14.endif
15.endfor
16. // arrange seeds in the list
17.for i in range(\(n\))do
18.for seed in remainSeeds[i] do
19. arrangeSeed(seed, \(S_{i}\))
20.endfor
21.endfor
22.return[\(S_{i}\)]
23.endfunction
```  
**Algorithm 3** Seed Mapping  
Remarks on the seed division algorithm are as follows: Firstly, each seed collected from the previous stage was used as the input to execute the target program sequentially (line 5). Then, the total weights of functions corresponding to each subtask on the execution trace corresponding to each seed were calculated respectively (lines 9-15). After that, the seed was assigned to the subtask with the highest weight (lines 17 and 18). Finally, to avoid scenarios where a subset of a specific seed corpus was empty, the top k seeds with the highest weights for each subtask was additionally assigned to that subtask (lines 25-29), even if the weight of other subtasks corresponding to this seed was higher. Figure 6: An example of seed mapping.
## 5 Discussion  
The application of IMPA\(\beta\) to the regular bridge (RB, Section 4.1) shows (see Figures 14-18) that, for each intensity level, and for each monitoring point observed (top of piers P1, P2 or P3), the difference between MPA and UPA (or even SPA) is negligible, and the results are similar to the ones from NLHA up to the design intensity (see Figures 16 and 17). The capacity curve obtained with IMPA\(\beta\)  
Figure 23: Case study—incremental curve (displacement-intensity) and capacity curves derived with IDA (maximum values of ur and Vb,x) or IMPA\(\beta\) (the design PGA was 0.35 g, corresponding to a transversal base shear of Vb,x \(\sim\) 13,000 kN).  
if compared with results from IDA (Figure 18), is suitable for the scope of investigating the structural response of a regular bridge throughout a wide range of seismic intensity.  
The application to the irregular bridge (IB, Section 4.2) shows (see Figures 20-22) that, unlike for the RB, the response evaluated with MPA and UPA (or even SPA) differs. In particular, observing results at the design intensity level (0.35 g), the following consideration can be drawn.  
MPA overestimates the response at P1 and underestimates the response at P2 and P3, whereas UPA underestimates the response at P1, and, at P2 and P3, gives results very similar to IDA (UPA underestimate of about 3% and MPA underestimate of about 20%). Therefore, by performing IMPA\(\beta\) according to the procedure described in Figures 4 and 5, the response obtained is conservative or very similar to IDA.  
Observing the capacity curves of Figure 23 (transversal displacement versus base shear of the pairs), it is evident that, for the IB the capacity curves from UPA and MPA define a relatively narrow bound for IDA results, and the capacity curve from IMPA\(\beta\) and IDA is very similar; for a very high intensity (beyond 0.6 g), IMPA\(\beta\) overestimates the response.
## 2 Materials and Methods
### Analyzed Methods
#### 2.1.5 Graph Augmented Normalizing Flows (GANF)  
GANF [12] is an anomaly detection method for multivariate time series that uses normalizing flows for density estimation. Normalizing flows are generative models \(f:\mathbb{R}^{d}\rightarrow\mathbb{R}^{d}\) that utilize a series of invertible and differentiable transformations to normalize complex data distributions to \"base\" distributions, whose densities are typically easy to evaluate (e.g., isotropic Gaussians) [12]. In addition to modeling the density of the time series using a normalizing flow, GANF incorporates a Bayesian Network to model the causal relationships among multiple multivariate time series \(\mathcal{X}=(\mathcal{T}_{1},\ldots,\mathcal{T}_{m})\). Given a training set \(\mathcal{D}=\{\mathcal{X}i\}i=1^{|\mathcal{D}|}\) of multiple time series, GANF aims to learn the adjacency matrix \(A\) of the Bayesian Network and, simultaneously, the graph-augmented normalizing flow \(\mathcal{F}:(\mathcal{X},A)\rightarrow\mathcal{Z}\), where \(\mathcal{Z}\) is a random variable with a \"simple\" (base) distribution [12]. Once \(\mathcal{F}\) is learned, the estimated density \(p(\mathcal{X})\) can be evaluated to identify anomalies in low-density regions of the base distribution. The dependency encoder of the model consists of a recurrent neural network to summarize the time series up to a given time step \(t\) and a graph convolution layer to learn a dependency representation, which is then used to condition a normalizing flow \(f\). For more information on the architectural details of GANF, please see [12]. As anomalies are rare by definition, it is typically assumed that their densities are low, and thus the estimated densities can be used as an anomaly score [12]. We included GANF as a deep-learning variant of a density-estimation-based anomaly detection method, given its ability to learn dependencies between multiple time series, although this feature is not used in the context of this comparison.
# A Novel Ship-Tracking Method for GF-4 Satellite Sequential Images
## 3 Experiments and Results
### Evaluation  
#### 3.2.1 Quantitative Evaluation Metrics  
To validate ship detection and tracking results, the performance of the proposed algorithm is quantitatively evaluated by using the standard evaluation measures: precision, recall and F-score. F-score is the harmonic mean of precision and recall. The precision, recall, and F-score are calculated as  
\[precision=\frac{TP}{TP+FP} \tag{24}\]  
\[recall=\frac{TP}{TP+FN} \tag{25}\]  
\[F-score=\frac{2\times precision\times recall}{precision+recall} \tag{26}\]  
where \(TP\), \(FP\) and \(FN\) are true positives, false positives, and false negatives, respectively.
## 5 Comparative Analysis based on the Design Guidelines
On the other hand, up to the author's knowledge, in the IP500 solution, end-devices remain in sleep state when they do not transmit or receive data, whilst intermediate devices (routers) are always ON, forwarding data or connected to other IP-based technologies. So, intermediate nodes are the \"energy bottlenecks\" of this proposal. However, this is solved by the usual application field of IP500, focusing only on providing a mesh solution for building automation, in which devices have unlimited access to electric current/power. Concerning 6 LoWPAN/RPL, studies in [12,34] propose an asynchronous mechanism whose operation sets that nodes acting as receivers wake up periodically to check the medium for any incoming transmission while senders occupy the entire channel transmitting a _preamble_. However, during the interval when the sender is waiting for the receiver to wake up, senders in ASES mode (IEEE 802.15.5) listen to the physical medium until the reception of the WN's messages. Thereby, ASES allows sender's neighbors to transmit their data within the same period dedicated by the sender node to receive the WN message, thus taking advantage of the available bandwidth. On the contrary, in 6 LoWPAN/RPL mechanism, the time period spent by the sender node transmitting its _preamble_ cannot be exploited by any other sender's neighbors to transmit data, which involves a significant disadvantage in comparison with ASES. On the other hand, future WMSNs integrating the IEEE 802.15.4e standard in its implementation will increase the network lifetime due to its TDMA-basedscheme, in which devices can switch to sleep state after transmitting. Unfortunately, there are not enough studies that evaluate and, therefore, validate the impact of the energy-consumption on each of the approaches based on TSCH and DSME in mesh networks. This restricts the knowledge of its real feasibility on large-scale topologies formed by resource-constrained devices. However, up to date the IEEE 802.15.5 standard offers ASES and SES, which have been evaluated in works [2] and [50], respectively, with promising results for WMSN. In particular, the research in [2] offers a performance evaluation in terms of energy-efficiency of ASES. This study [2] also presents two well-known MAC protocols denoted as Berkeley MAC (BMAC) [66] and XMAC [67], which operate by using their own _low-power listening_ techniques, and therefore follow the same premises as the one proposed by 6 LoWPAN/RPL. Although results show that the average time nodes remain in active mode is similar regardless of the type of protocol, ASES is the technique assuring a less energy consumption per node, which in turn guarantees a larger network lifetime than BMAC and XMAC protocols.
# Development of User-Participatory Crowdsensing System for Improved Privacy Preservation
## 4 Implementation of Proposed System
### User Application  
The user application is used by the data requester to send a request to the \(SP\), or by the data provider to retrieve the request and provide sensing data. It also generates and manages RSA-2048 asymmetric cryptographic key pairs required for participating in crowdsensing systems.  
The data requester inputs the description, schema, and reward amount of the requested sensing data through the user application, which transmits them with the data requester ID to the \(SP\). The data provider retrieves the sensing data provision request through the user application and provides the sensing data. In this process, the user application requests a session key, encrypts and transmits sensing data, and generates a CoAP message.
# A Lookahead Behavior Model for Multi-Agent Hybrid Simulation
## 5 Case and Experiments
### Case Scenario and Experiment Setup  
A simple military air defense scenario is designed with both red and blue units. The red side mainly contains pairs of radar and artillery deployed in the same position, while the units of the blue side are the aircrafts. The detection model of radar uses a simplified constant-delay model based on the detection radius, that is, after the target enters the detection range for a constant time, the radar performs a detection to obtain the target information. The artillery attacks the target after another constant time of the target detection. The damage on the aircraft is not considered. The aircraft's motion model is a continuous model, using uniform linear motion to update the position. Radar perception and artillery attacks are both discrete models.  
In order to simplify the implementation, two types of agents are established: StaticAgent on the red side and MovingAgent on the blue side. StaticAgent simulates the capabilities of detection and attacking of radar and artillery, respectively, whereas MovingAgent simulates the process in which the aircraft performs movements.  
Experiments are carried out to simulate the process of sense, move, and attack of the two kinds of agents. The experiment is as follows: set the initial and ending positions of all MovingAgents and perform the moving action \(a_{move}\) to move from the initial position to the destination at a fixed speed. When the destination is reached, the agent resets the initial and ending positions and continues the movement. StaticAgent detects the MovingAgents and performs the attack action \(a_{attack}\).  
Four sets of experiments are conducted to evaluate the efficiency of LBM, namely:  
1. Fixed time synchronization-based simulation (FS);
2. Variable time synchronization-based simulation (VS);
3. Complete resynchronization-based simulation (CR);
4. Partial resynchronization-based LBM simulation (LBM-PR).  
In the LBM model, the agent spends its time mainly on performing state updates, executing the behavior cycle, and forecasting the synchronization interval. Assuming that each agent finishes the three tasks for constant time durations, the number of state updates, the number of executed cycles, and the number of lookaheads will affect the time spent by the agent model. Therefore, this paper uses these measurements as the LBM performance indicators. Taking account of random factors, this experiment will use the average of these measurements as the ultimate performance metric.  
The parameters of this experiment are shown in Table 4.
# Ferroelectric Materials: A Novel Pathway for Efficient Solar Water Splitting
## 2 Overview of Solar Water Splitting
### Strategy for High Solar-to-Hydrogen (STH) Efficiencies  
The energy conversion efficiency in solar water-splitting cells is usually evaluated by various methods such as standard solar-to-hydrogen (STH) conversion efficiency (\(\eta_{STH}\)), quantum efficiencies such as the incident photon-to-current efficiency (IPCE), photon-to-current efficiency (ABPE), and the absorbed photon-to-current conversion efficiency (APCE) [21; 22]. Among these, we will further examine the standard \(\eta_{STH}\) and ABPE in greater detail.  
The standard \(\eta_{STH}\) is the ratio between the total energy generated and the total energy input in the sunlight irradiation of AM 1.5 G (air mass at a solar zenith angle of 48.2\({}^{\circ}\)), which can be calculated using following the equation:  
\[\eta_{STH}\ =\frac{Total\ Energy\ generated}{Total\ energy\ input}=\frac{ \Delta G\times r_{H_{2}}}{P_{sun}\times S} \tag{1}\]  
where \(\Delta G\) is the Gibbs free energy (237 kJ/mol), \(r_{H}\) is the rate of hydrogen production in moles per second, \(P_{sun}\) is the incident light intensity (100 mW/cm\({}^{2}\)) and \(S\) is the illuminated area of the photoelectrode (cm\({}^{2}\)).  
The ABPE can be calculated from the J-V curve of the photoelectrode while an external bias is applied to the two electrodes.  
\[\text{ABPE }(\%)\ =\ \left[\frac{J\ \left(mA\,/\,cm^{2}\right)\ \times \left(1.23-V_{bias}(V)\right)}{P_{in}\ (mA\,/\,cm^{2})}\right]_{AM\ 1.5G}\times 100\ \% \tag{2}\]
## 3 Results
### Promoted Ripening in FaMADS1-RNAi Strawberry  
As shown in Figure 4A, _FaMADS1_-RNAi greatly promoted the ripening progression of strawberry fruit. The fruit on _FaMADS1_-RNAi plants became partially red (nearly 75%), whereas the control fruit remained in the white stage at 7 d after injection. _FaMADS1_-RNAi fruit displayed a substantial increase in anthocyanin content and a decrease in firmness (Figure 4B,C). Furthermore, _FaMADS1_-RNAi led to a decrease in protopectin and an increase in soluble pectin (Figure 4D,E). Furthermore, _FaMADS1_-RNAi increased the levels of esters (methyl hexanoate; 1-methyl hexanoate; methyl 2-octynoate; octyl butyrate; octyl 3-methylbutyrate; (3,7,11-trimethyldodeca-1,6,10-trien-3-yl) formate; 3,7,11-trimethyl-1,6,10-dodecatrien-3-olacetate and 5-octyloxolan-2-one), ketones [DMMF and 1-(2,6,6-trimethyl-1-cyclohex-1-yl)-1-penten-3-one], acids (octanoic acid and 3-hydroxydodecanoic acid), terpen (2,3-dihydro  
Figure 4: Fruit properties of _FaMADS1_-RNAi strawberry. RNAi fruit was already red when the control fruit was white at 7 d after injection (**A**). Anthocyanin content (**B**), firmness (**C**), protopectin (**D**), and soluble pectin content (**E**) were detected in control (empty vector) and _FaMADS1_-RNAi strawberry fruit at 7 d after injection. Error bars represent standard deviation (SD, \(n\) = 3). Asterisks indicate values significantly different by Student’s \(t\) test from the control samples (* \(p\) < 0.05; ** \(p\) < 0.01). \(1H\)-indene), and benzene [(4a5-_cis_)-2,4a,5,6,7,8,9,9a-octahydro-3,5,5-trimethyl-9-methylene-\(1H\)-Benzocyclloheptene]. Meanwhile, we observed increased levels of some alcohol, aldehyde, and olefin in _FaMADS1_-RNAi strawberry fruit (Table 1). This increase in aroma indicated that a metabolic boost facilitated fruit ripening in _FaMADS1_-RNAi lines.