esa-sceva/satcom-chunk-collection huggingface dataset:
# Statistical Power Analysis in Reliability Demonstration Testing: The Probability of Test Success
## 4 Comparison of the Calculation Methods for the Probability of Test Success
### Comparison Using Success Run Tests  
For calculating the \(P_{\text{ls}}\) of an SR test, the general method, the exact method, and the test simulation method can be used. Due to the lack of an MLE and confidence level estimation, the general calculation method coincides with the method of test simulation. As no MLE takes place during the SR test, no influence of an estimation method is expected and the general method converges to the exact method. This can be seen in Figure 4 for a requirement of \(R_{\text{r}}=C_{\text{r}}=90\%\).  
The MCS of the general method converges to the exact method; 100,000 iterations ensures reasonable accuracy in this scenario with the prior knowledge as Weibull-distributed failures, with \(b=3\) and \(T=1\) and no allowed failures for the lifetime requirement of \(t_{\text{r}}=0.2\) (\(s=0.577\)). When scenarios using different prior knowledge and different requirements are analyzed, and when failures are allowed, similar behavior is shown, leading to the conclusion that the exact method is to be preferred for calculating the \(P_{\text{ls}}\) of an SR test. In addition, the exact method is much more effortless to use, as no MCS iteration has to take place.
## 4 Results Analysis and Discussion
### Temperature Field Characteristics of Single-Channel Deposition  
As depicted in Figure 10, the temperature field distribution of the single-channel IN718 deposited layer at various times was determined using simulation calculations of the process with a powder feeding rate of 0.2 g/s, laser power of 500 W, and a scanning speed of 10 mm/s. Within 0.05 s of the start of the process, the shape of the deposited layer has not yet stabilized, the temperature is highest in the center region of the laser spot, the surface temperature decreases rapidly away from the center of the laser spot, and the temperature field distribution on the surface of the deposited layer is approximately axisymmetric. Comparing Figure 10c,d, it is apparent that after 0.30 s of processing, the shape of the deposited layer basically develops and stabilizes, and the \"trailing\" phenomena of the temperature field distribution of the deposited layer becomes increasingly apparent.  
Figure 10: Temperature contour of single path deposition at different moments. (**a**) 0.05 s; (**b**) 0.15 s; (**c**) 0.30 s; (**d**) 0.40 s.  
Figure 9: Deposition channel size under different laser power. (**a**) Sedimentary channel height varies with scanning speed; (**b**) Sedimentary channel width varies with scanning speed.  
Figure 11 depicts the 3D melt pool and deposition layer morphology at the moment corresponding to Figure 10. As the process continues, the deposited layer height grows, and the melt depth and melt breadth of the melt pool expand, eventually reaching a steady state. Comparing the deposited layer morphology and melt pool shape in Figure 11b-d, it is evident that the deposited layer height and melt pool size are still increasing from Figure 11b,c, while the deposited layer height and melt pool size are basically unchanged from Figure 11c,d and have reached the steady state. In this instance, the measured time for the deposition layer height and melt pool size to achieve stability is 0.25 s, and the forming start section length before the deposition layer reaches stability is 2.50 mm. In order to precisely control the shape and size of the laser deposition layer in a given process, the processing forming start section must be at least 2.50 mm in length.
# An Overview of Cooperative Driving in the European Union: Policies and Practices
## 4 Current State of the Art of C-ITS Systems
### C-Roads Pilot Sites
#### 4.1.3 The Netherlands  
The Dutch pilot area is situated in the south of the Netherlands. The tested applications are Day-1 type: RWW, VSGN, and GLOSA services will be implemented in a hybrid framework combining both ITS-G5 and cellular network technologies. Moreover, infotainment tasks and different use cases of the logistic service multimodal cargo transport optimization (MCTO) are planned to be tested.  
The project coordinator is the Dutch Ministry of Infrastructure and Water Management, which has set up a test site project management team (TPMT) for handling test activities. The Provinces of Noord-Brabant and Utrecht are involved. The rolling-out phase is planned to last for the whole of 2019. Moreover, road shows will be performed in 2020.  
Finally, the Netherlands plays a key role in two other European projects, namely C-ITS Corridor and InterCor-Interoperable Corridors [82]. Within the InterCor framework (in which the other Member States involved are Belgium, France, and the UK), two relevant demonstration events held in the Netherlands should be mentioned. The first was a testfest, which took place in July 2017 and involved RWW, VSGN, and PVD services. It was followed by the GLOSA Pre-Testfest in June 2018, aimed at evaluating the interoperability level reached in implementing GLOSA services.
# Replay Speech Detection Based on Dual-Input Hierarchical Fusion Network
## 4 Results
### The System Performance Compared with State-of-the-Art Systems  
Table 4 presents the experimental results of the top five teams in the ASVspoof 2021 Challenge [7]. As can be seen from the results of the participating teams, only the top result had a min t-DCF below 0.7 and an EER below 25% in the PA evaluation set, demonstrating that developing a replay speech detection system from a simulated speech corpus that perfectly matches the real environment is a significant challenge.  
Among them, T07, a team from Kunshan Duke University [39], achieved the best performance in the PA track. They proposed using the difference between the codec-filtered audio and the original audio as input to the model, using codecs such as WORLD [40] and MelGAN [41]; they also used speech enhancement methods such as velocity perturbation and reverberation to augment the model training data; in addition, several outlier detection systems were used as classifiers, with the final submission being an average of various systems. T23, a team from Russia, proposed using a variety of front ends, including Mel-scaled short-time Fourier transform of an audio signal's linear or rectangular frequency filter banks, and trainable transforms such as LEAF or SinConv [24]; moreover, several deep neural networks such as ResNet18, LCNN9, RawNet2, and their modifications, were used as classifiers, and microphone and room impulse responses were used for data enhancement. The final system is a fusion of several classification models [42].  
Instead of using speech enhancement, our proposed method takes both the original audio signal and the time-reversed audio signal as inputs to the model and uses HFN as the classifier, with the fusion system being a weighted average of the three single system scores of HFN + Spec, HFN + LFCC, and HFN + CQT. Compared with the best results, the proposed system improved the min t-DCF by 1.7% and achieved a similar level of EER.
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
###### Abstract  
The ever increasing proliferation of wireless objects and consistent connectivity demands are creating significant challenges for battery-constrained wireless devices. The vision of massive IoT, involving billions of smart objects to be connected to the cellular network, needs to address the problem of uninterrupted power consumption while taking advantage of emerging high-frequency 5G communications. The problem of limited battery power motivates us to utilize radio frequency (RF) signals as the energy source for battery-free communications in next-generation wireless networks. Backscatter communication (BackCom) makes it possible to harvest energy from incident RF signals and reflect back parts of the same signals while modulating the data. Ambient BackCom (Amb-BackCom) is a type of BackCom that can harvest energy from nearby WiFi, TV, and cellular RF signals to modulate information. The objective of this article is to review BackCom as a solution to the limited battery life problem and enable future battery-free communications for combating the energy issues for devices in emerging wireless networks. We first highlight the energy constraint in existing wireless communications. We then investigate BackCom as a practical solution to the limited battery life problem. Subsequently, in order to take the advantages of omnipresent radio waves, we elaborate BackCom tag architecture and various types of BackCom. To understand encoding and data extraction, we demonstrate signal processing aspects that cover channel coding, interference, decoding, and signal detection schemes. Moreover, we also describe BackCom communication modes, modulation schemes, and multiple access techniques to accommodate maximum users with high throughput. Similarly, to mitigate the increased network energy, adequate data and power transfer schemes for BackCom are elaborated, in addition to reliability, security, and range extension. Finally, we highlight BackCom applications with existing research challenges and future directions for next-generation 5G wireless networks.  
backscatter communications; ambient backscatter; battery-free communications; wireless communications; energy harvesting; mm-waves; IoT; 5G +
Footnote †: journal: Electronics Electronics
## 3 Results
### Temporal Dynamics  
Satellite-derived soil moisture data were compared with precipitation records to analyze the temporal dynamics of soil moisture fluctuations. Figure 7 illustrates this comparison, with soil moisture data (black line) overlaid on precipitation data (gray bars). The analysis revealed that satellite data effectively captured soil moisture fluctuations following precipitation events, with notable increases in soil moisture levels immediately after rainfall.  
Figure 6: Top- and bottom-performing sensors: correlation analysis with satellite data (2021–2022).  
Figure 7: Satellite-based soil moisture and precipitation by season (2021–2022).  
For the 2021 season, the soil moisture data show a strong response to precipitation events, with notable increases following rainfall. Periods of high precipitation correspond to sharp rises in soil moisture, which gradually declines during dry periods, reflecting typical hydrological processes. Similarly, the 2022 season exhibits consistent patterns, with soil moisture levels closely tracking precipitation events. However, variations in the magnitude of soil moisture compared to 2021 may indicate differences in the seasonal rainfall intensity, distribution, or land surface conditions. These results suggest that integrating meteorological precipitation records further enhances the understanding of soil moisture fluctuations, emphasizing the satellite data's applicability for large-scale and long-term environmental monitoring. In addition to this, it also highlights the reliability of remote sensing technologies for capturing soil moisture dynamics on a broader temporal scale.
# Design and Testing of a Wearable System for Monitoring Car Drivers
## 2 Materials and Methods
### Conceptual Design  
A monitoring system is proposed in this paper that consists of three wearable device supports, namely a wristband, a headband, and a torso patch, as indicated in Figure 2. The wristband on the wrist serves as the central hub, containing several sensors, while the headband and torso patch house mainly IMU sensors. The used IMU sensors are IMU MPU6050 (2.5 \(\times\) 3 \(\times\) 0.83 mm, +16 g range) [23], integrated in Arduino 33 IoT [24], the heart rate sensor Analog MAX30102 (5.6 \(\times\) 3.3 \(\times\) 1.55, 1000 pulse/s range) [25], and the galvanic skin Grove GSR sensor (20 \(\times\) 20 mm and 2000 mS range) [26]. The wristband houses a heart rate sensor and a skin response GSR sensor that can be used to monitor physiological signals indicating stress and aggressiveness. The wristband is provided with an IMU to monitor the arm motion at the level of the wrist. The torso unit uses an IMU to monitor the motion of the torso; likewise, the head unit is equipped with another IMU. Each unit is provided with an Arduino 33 IoT (45 \(\times\) 18 \(\times\) 2 mm) [18] for data acquisition and handling.  
The selected sensors communicate wirelessly via Bluetooth to transmit data to a central processing unit, according to the scheme in Figure 3. The system is powered by  
Figure 2: Location of the main sensors in the proposed monitoring system on the body parts of a car driver.  
rechargeable batteries, ensuring uninterrupted operation during driving. Each unit can be considered independent as they are all equipped with a proper Nano Arduino board and power source battery. However, the data flow can be shared among the three Nano Arduino boards via Bluetooth in order to be used for data elaboration, storage, and alerts.  
The data acquisition focuses on evaluating the system's ability to accurately capture and process physiological data, particularly in detecting aggressive driving behaviors using data coming from the wristband, torso, and headband units. Several tests were carried out to ensure the sensors' accuracy, synchronization, and reliability. In particular, the test experiences gave the following results.  
The heart rate sensor MAX30102 had some difficulties in accurately capturing heart rate variability (HRV) as a significant indicator of stress and aggression. In addition, its difficult installation reduced its reliability, meaning it was not completely successful.  
The skin conductance sensor (Grove GSR) also had some difficulties in accurately measuring skin conductance data as an indicator of the driver's emotional state. In addition, its difficult installation reduced its reliability, meaning it was not completely successful.  
The IMU sensors in the wristband, torso box, and headband were successful in properly detecting the movements of the body parts where they were installed. The data showed consistent and reliable detection of acceleration and angular velocity, as significant measures for identifying sudden and aggressive driving maneuvers.
## 5 Testbed Demonstration Setup
### Seamless Satellite Integration into Standard 3 GPP Network Architecture
The testbed demonstration in Figure 13 showed data from five sensors on a screen, representing active devices such as a phone, a wearable, a car, and a fixed IoT sensor aggregator, all were connected and exchanged information as it was routed through the combined space-and-ground network to the core network in Berlin. Figure 12: Seamless satellite integration into 3 GPP network architecture: hardware setup of the SATis5 testbed. (**a**) The 1.2 m Ku-Band antenna system installation in Killarney, Ireland; (**b**) iDirect Velocity Intelligent Gateway (IGW) satellite hub platform installation in Betzdorf, Luxembourg.
## 3 Fuzzy Set Theory-Based Risk Assessment System for Navigational Obstacles
### System Development
#### 3.5.2 Determination of Fuzzy Set  
A collision risk assessment model was created using Monte Carlo simulation. An error of 5-10% occurs when this technique is applied [35]. Therefore, it was applied to the ranges of the collision risk index, pollution risk index, and comprehensive risk index of the collision risk assessment model under the assumption that this error introduces fuzziness. The accident risk level [32] defined in the Marine Traffic Safety Information System (MTIS) of the Korea Maritime Traffic Safety Authority was used as the linguistic variable corresponding to the overall risk and index range of the system:  
* Low: Accident risk is less than 50%.
* Moderate (yellow): Accident risk is greater than 50% and less than 85%.
* Threat: Accident risk is greater than 85% but less than 95%.
* Danger (red): Risk of accident is at least 95%.  
The following fuzzy sets were determined, as shown in Figure 11: (a) fuzzy set for the collision risk assessment model, (b) fuzzy set for the pollution risk assessment model, and (c) fuzzy set theory-based navigational obstacle risk assessment system that simultaneously considers both the collision risk and pollution risk.  
Figure 11: Fuzzy membership function of the risk assessment system for navigational obstacles. **(a)** Collision risk, **(b)** Pollution risk, and **(c)** Comprehensive risk.  
Table 6 lists the range and shape of the membership function (MF) according to the linguistic variables corresponding to each fuzzy set.
###### Abstract  
In recent years, Artificial Intelligence (AI) provided essential tools to enhance the productivity of activities related to civil engineering, particularly in design, construction, and maintenance. In this framework, the present work proposes a novel AI computer vision methodology for automatically identifying the corrosion phenomenon on roofing systems of large-scale industrial buildings. The proposed method can be incorporated into computational packages for easier integration by the industry to enhance the inspection activities' performance. For this purpose, a dedicated image database with more than 8k high-resolution aerial images was developed for supervised training. An Unmanned Aerial Vehicle (UAV) was used to acquire remote georeferenced images safely and efficiently. The corrosion anomalies were manually annotated using a segmentation strategy summing up 18,381 instances. These anomalies were identified through instance segmentation using the Mask based Region-Convolution Neural Network (Mask R-CNN) framework adjusted to the created dataset. Some adjustments were performed to enhance the performance of the classification model, particularly defining an adequate input image size, data augmentation strategy, Intersection over a Union (IoU) threshold during training, and type of backbone network. The inferences show promising results, with correct detections even under complex backgrounds, poor illumination conditions, and instances of significantly reduced dimensions. Furthermore, in scenarios without a roofing system, the model proved reliable, not producing any false positive occurrences. The best model achieved metrics' values equal to 65.1% for the bounding box detection Average Precision (AP) and 59.2% for the mask AP, considering an IoU of 50%. Regarding classification metrics, the precision and recall were equal to 85.8% and 84.0%, respectively. The developed methodology proved to be extremely valuable for guiding infrastructure managers in taking physically informed decisions based on the real assets condition.  
corrosion; industrial buildings; deep-learning; Mask R-CNN; instance segmentation; Unmanned Aerial Vehicles (UAVs) 2023
## 4 Proposed Methodology
### Cluster Head Selection
#### 4.1.4 FCSEO Algorithm for CH Selection  
Residual energy \((G)\) is considered as the main parameter of WMSN which decides whether the SN is to be selected as CH or hop nodes. In a fuzzy system, the linguistic variables namely Very Higher (\(VH\)), Higher (\(H\)), Medium (\(M\)), Lower (\(L\)) as well as Very Lower (\(VL\)) are introduced to delineate the residual energy. Figure 3 describes the fuzzy rule of residual energy (input variable). Moreover, radio energy dissipation is also considered to be a significant parameter for CH selection. Centrality \((\partial_{j})\), is a parameter that calculates how central and the role of SN inside the cluster. The SN, which is located at center of cluster, has more possibility to be selected as CH. So, the central location of cluster is determined by the following expressions.  
\[C_{enter}A=a+\frac{w}{2} \tag{25}\]  
\[C_{enter}B=b+\frac{h}{2} \tag{26}\]  
Figure 3: (**a**) fuzzy set for residual energy, (**b**) fuzzy set for centrality and (**c**) fuzzy set for provability of CH selection.  
The term \(w\) indicates the width of cluster and \(h\) depicts height of cluster. Also, \((a,b)\) represent the left down corner position of the cluster. Then the location of the cluster center is found by measuring the centrality of SN using Euclidean distance, which is given by,  
\[D\big{(}P_{ja},\ Cluster_{center}\big{)}=\sqrt{\big{(}P_{ja}-C_{ center}A\big{)}^{2}+\big{(}P_{jb}-C_{enter}B\big{)}^{2}} \tag{27}\]  
From the above equation, \(Ath\) and \(Bth\) positions of SN are denoted as \(P_{ja}\) and \(P_{jb}\). Table 2 presents the 9 fuzzy mapping rules for the selection of CH.  
The FCSEO parameters \(G\) and \(\partial_{j}\) represent the input variables residual energy and centrality; the parameter \(\Delta\partial_{j}\) represents the output variable probability of CH selection.  
Figure 3a illustrates the fuzzy rule for generating residual energy (input variable) where purple color signifies the membership function with very high value (VH), blue indicates higher value (H), Pink represents medium value (M), Green and Orange represents low (L) and very low value (VL).  
Figure 3b indicates the fuzzy rule generation based on centrality. Pink and Green lines denote very high (VH) and high (H) values, Blue and purple represent Medium (M) and low values (L) respectively.  
Figure 3c portrays the probability of selecting the cluster head; where green and pink signifies very high (VH) and high values (H), blue indicates medium value(M), orange and green represents low (L) and very low (VL)values respectively.
# Optimized Deployment of Generalized OCDM in Deep-Sea Shadow-Zone Underwater Acoustic Channels
## 6 Discussion
### Summary and Future Research Recommendations  
The simulation and sea trial results demonstrate that GOCDM achieves a remarkable performance in deep-sea shadow-zone communication. Since the trials were conducted in winter when surface ducts rarely form, only bottom-bounce sound channels existed during shadow-zone communication, resulting in relatively short overall channel delays--a favorable condition for communication. However, during summer shadow-zone communication, surface ducts are highly likely to exist, and signals may also reach the shadow zone through scattering. Under such conditions, the total channel delay could extend to several seconds, significantly degrading communication performance. Therefore, I suggest that further research should first focus on designing an appropriate frame structure to address these long delays, and then we should conduct simulations using tools like Bellhop. Subsequently, we should conduct a series of sea trials under summer conditions with surface ducts to validate system performance.