esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This paper has investigated the performance of an active vibration isolation system fitted to a diesel generator in a floating tugboat. The system was fitted to the generator set without making any changes to the existing machinery layout. It thus effectively complemented the existing passive  
Figure 8: Effectiveness of the active control at each engine half-order.  
Figure 7: Experimental results: (**a**) Spectra of vibration with and without control, (**b**) Acoustic pressure in the water with and without control.  
vibration isolation system. This system consisted of six inertial electrodynamic actuators and a DSP-based controller employing an adaptive feedforward control strategy. The reference signal for the controller was derived from an optical tachometer on the shaft between the engine and the generator. The signal was passed through a half-order generator to generate all the harmonics in the vibration response. Using the responses from six accelerometers placed next to the actuators, a six-input and six-output decentralized adaptive control strategy was adopted. Six accelerometers were also placed directly below the passive isolators to give a measure of the hull vibration below the generator, and a hydrophone was placed in the water to give an indication of the sound radiated. The active vibration isolation experiment was conducted when only the diesel engine generator was working in the tugboat.  
The experimental results show that the combination of the active control system and the passive isolators is effective in reducing the global vibration on the frames below the generator, with vibration reductions of more than 10 dB at several engine orders when the active control system is switched on. The reduction in the acoustic pressure at the hydrophone position due to active control is up to 8 dB for some engine orders although there is a marginal increase at some other frequencies.  
Overall, the study has demonstrated that a self-contained active control system can be retrofitted to a marine diesel generator set where there is limited space. Using inertial actuators and accelerometers, it has been shown that it is possible to achieve appreciable attenuation of the vibration transmitted to the hull at integer multiples of the half engine orders, complementing the attenuation due to the passive vibration isolators. As the active control system proved to be effective on one generator set, the next step in this work is to extend the system to both generator sets, and then to perform trials in harbor and then at sea.  
Methodology, T.Y. and X.L.; software and validation, M.Z. and X.L.; experimental work, T.Y., L.W., M.Z. and X.L.; writing--original draft preparation, L.W.; writing--review and editing, T.Y. and M.J.B.; supervision, Z.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, No. 51375103.  
The authors declare no conflict of interest.
# Real-Time Hybrid Multi-Sensor Fusion Framework for Perception in Autonomous Vehicles
## 4 Hybrid Sensor Fusion Algorithm Overview
### Testing Obstacle Detection and Tracking using Kalman Filtering
#### 4.2.1 Radar Beat Signal Data Processing
The sliding window in a 2D CFAR includes: the Cell Under Test (CUT) or current cell tested for target presence, the guard cells (GC) or cells around the CUT that prevent the leakage of the target signal on to the surrounding cells hence negatively affecting the noise estimate, and finally the reference or training cells (RC) or cells covering the surrounding of the CUT. The local noise level is estimated by averaging the noise under the training cells. If the signal in CUT is smaller than the threshold, it is removed. Anything above the threshold is considered a detection. Finally, we have radar CFAR detection with range and Doppler velocity information but object detection and tracking in real time, is a computationally expensive process. Therefore, we cluster the radar detection that belongs to the same obstacle together to increase the performance of our pipeline. We use a Euclidean distance-based clustering algorithm. In this method, all the radar detection points that are within the size of the target are considered one cluster. The cluster is assigned with a range and velocity at the center equal to the mean of the ranges and velocities of all the cluster detection.
## Abbreviations  
The following abbreviations are used in this manuscript:  
3 GPPThird-Generation Partnership Project  
5 GFifth Generation  
6 GSixth Generation  
5 GC5G Core  
CGNATCarrier-Grade Network Address Translation  
CUCentral Unit  
DNData Network  
DUDistributed Unit  
FR1 Frequency Range 1  
GEOGeosynchronous Equatorial Orbit  
gNBNext-Generation Node B  
HARQHybrid Automatic Repeat Request  
ICMPInternet Control Message Protocol  
IPInternet Protocol  
ISLInter-Satellite Links  
LANLocal Area Network  
LEOLow Earth Orbit  
MACMedia Access Control  
MEOMedium Earth Orbit  
MNOMobile Network Operator  
NRNew Radio  
NTNNon-Terrestrial Network  
OAIOpenAirInterface  
PRBPhysical Resource Block  
RARandom Access  
RANRadio Access Network
## 3 Empirical Results and Analysis
### Robustness Test
#### 3.2.3 Replacing the Explained Variable Test and the PSM-DID Estimation
To sum up, through a series of robustness tests, such as the placebo test, the counterfactual test, the explained variable replacement test and the PSM-DID estimation, this paper concludes that the promotion effect of the APCP Action Plan policy on \(GTFP\) is robust.
## Materials and Methods
### The Models: UTOPIA (University of Torino model of land Process Interaction with Atmosphere)  
The LSM selected for this study is the UTOPIA (University of Torino model of land Process Interaction with Atmosphere [22] (A minimal web page containing basic information about the UTOPIA is available here: [[http://personalpages.to.infn.it/~](http://personalpages.to.infn.it/~)]([http://personalpages.to.infn.it/~](http://personalpages.to.infn.it/~)){}|classardo/utopia/utopia.html)). UTOPIA is a third-generation land surface model developed at the University of Torino (Physics Department) and is the upgraded version of the LSPM (Land Surface Process Model [35; 36]). It is a diagnostic one-dimensional model studying the interactions at the interface between atmospheric surface layer, vegetation and soil.  
Both models, UTOPIA and LSPM, were tested using measured data from experimental campaigns or coupled with an atmospheric circulation model.  
The UTOPIA can be categorized as a big leaf model, meaning that a single vegetation element contributing to the various processes is considered, without considering its real extension. It is a soil multilayer model, where the number of the layers can be defined by the user. UTOPIA works on a single point in which the only direction allowed is the vertical one. Energy exchanges are evaluated by a resistance network taking into account the kind of surface. Momentum, heat and water vapor fluxes are the main processes considered. In addition, the model simulates the soil thermal, hydrological and photosynthetic processes.  
UTOPIA needs, as mandatory requirements, some data to run: air temperature, specific or relative humidity, the two horizontal components of wind speed, precipitation rate (rain and/or snow), low and total cloudiness or solar global incoming radiation. Other variables, if not provided in input, are calculated in specific routines of the model. All UTOPIA simulations were initialized on 1 January 2007, e.g., about one year before the beginning of observations, in order to avoid any spin-up problem. In this analysis, the first year of UTOPIA simulations was not considered.  
Vegetation parameters were initialized depending on vegetation code, which is a number identifying the main vegetation types in the world. The dataset used in the UTOPIA model is an extension [22] of the Land Cover/Vegetation type taken from Reference [20], and the code numbers employed for the UTOPIA simulation were: 10 (irrigated crop) and 26 (vineyards).  
Soil parameters were taken from an extension [22] of the [37] soil texture table, where they are classified by soil type code. The types of soil are determined from the percentage of sand, silt and clay through the [38; 39] soil textural database.
# Synthesis and Characterization of Unsaturated Succin Acid Biobased Polyester Resins
## 2 Materials and Methods
### Characterization Methods
#### 2.3.3 Size-Exclusion Chromatography (SEC)  
SEC measurements were conducted to determine the molecular weight M\({}_{\rm n}\), M\({}_{\rm w}\) and the dispersity index D of the synthesized resins. Chloroform at 25 \({}^{\circ}\)C was used as eluent, with a flow rate of 1 mL min\({}^{-1}\). The SEC was composed of a Waters 600 high-performance liquid chromatographic pump, Waters Ultrastragel columns HR-1, HR-2, HR-4E, HR-4, and HR-5, and a Shimadzu RID-10A (Kyoto, Japan) refractive index detector. Nine PS standards between 1 and 300 kg mol\({}^{-1}\) were used for calibration. The prepared solutions had a concentration of 12 mg mL\({}^{-1}\), the injection volume was 150 \(\mu\)L, and the total elution time was 60 min.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**MA Feed (eq)**} & \multicolumn{3}{c}{**SA/MA**} & \multirow{2}{*}{**EG (eq)**} & \multirow{2}{*}{**PEG200 (eq)**} & \multirow{2}{*}{**Biobased Content (mol\%)**} \\  & & & & & & \\ \hline PESM 50-50 & 0.5 & 0.5 & 48-52 & 1.25 & - & 78 \\ PESM 40-60 & 0.6 & 0.4 & 42-58 & 1.25 & - & 73 \\ PESM 25-75 & 0.75 & 0.25 & 28-72 & 1.25 & - & 67 \\ PPEGSM 50-50 & 0.5 & 0.5 & 53-47 & - & 1.25 & 78 \\ PPEGSM 40-60 & 0.6 & 0.4 & 44-56 & - & 1.25 & 73 \\ PPEGSM 25-75 & 0.75 & 0.25 & 30-70 & - & 1.25 & 67 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition of the unsaturated polyester resins synthesized in the study.
###### Abstract  
Underwater acoustic communications are limited by the following channel impairments: time variability, narrow bandwidth, multipath, frequency selective fading and the Doppler effect. Orthogonal Frequency Division Modulation (OFDM) is recognized as an effective solution to such impairments, especially when optimally designed according to the propagation conditions. On the other hand, OFDM implementation requires accurate channel knowledge atboth transmitter and receiver sides. Long propagation delay may lead to outdated channel information. In this work, we present an adaptive OFDM scheme where channel state information is predicted through a Kalman-like filter so as to optimize communication parameters, including the cyclic prefix length. This mechanism aims to mitigate the variability of channel delay spread. This is cast in a protocol where channel estimation/prediction are jointly considered, so as to allow efficiency. The performance obtained through extensive simulations using real channels and interference show the effectiveness of the proposed scheme, both in terms of rate and reliability, at the expense of an increasing complexity. However, this solution is significantly preferable to the conventional mechanism, where channel estimation is performed only at the receiver, with channel coefficients sent back to the transmit node by means of frequent overhead signaling.  
underwater acoustic communications; OFDM; interference; estimation; prediction; Kalman filtering 202312552[[https://doi.org/10.3390/electronics12071552](https://doi.org/10.3390/electronics12071552)]([https://doi.org/10.3390/electronics12071552](https://doi.org/10.3390/electronics12071552))
## 1 Introduction
Previous studies have attempted to synthesize and analyze the literature without providing a coherent taxonomy to help future researchers map the research directions and relevant issues [27; 28; 29; 31; 2]. Furthermore, these studies were often focused on IoT adoption challenges and neglected to discuss the benefits [27; 29; 30]. IoT adoption benefits require special attention due to their impact on the IoT adoption decision. Finally, previous studies have not provided a sufficient analysis to understand the main factors influencing IoT adoption in organizations, except the study [31] that attempted to determine the factors based on UTAUT constructs, as seen in Table 1. Therefore, this review aims to bridge the current literature gap regarding IoT adoption in organizations and offers a recent picture of its current status through the following steps:  
1. Analyzing and building on existing research to provide a coherent taxonomy and landscape for future IoT research in organizations. 2. Surveying the IoT adoption benefits and challenges to provide insight into the future requirements of IoT adoption and deployment in organizations. 3. Identifying the critical factors that influence IoT adoption in organizations. This paper is organized as follows: Section 2 details the methodology of the review. Section 3 summarizes the literature landscape and outlines the coherent taxonomy. Section 4 presents and classifies IoT benefits for organizations. Section 5 discusses the challenges that hinder IoT adoption in organizations. Section 6 presents the theoretical models used in prior studies and identifies the critical factors of IoT adoption. Section 7 discusses the findings and limitations of the study. The final section presents the conclusions of this study.
## 3 Results
In (9), the second term is added by the interferometry, which can be interpreted as the amplification of the signal intensity \(A\) by \(B\cos\!\left[\frac{\omega}{c_{0}}\!\left(L_{\text{Obj}}-L_{\text{Ref}}\right)\right]\), which can be considered as a gain with phase term. Although the cosine factor, which includes the phase difference, generates strong interference fringes during the frequency scan, they can be averaged out by post-processing. We experimentally estimated the homodyne gain. After replacing the sample with a switchable metallic mirror (MS), the signal intensity due to the metallic mirror was measured, with and without the reference mirror M3. Without M3, the contribution of the MS to the signal intensity was 0.17 mV (0.17 mV and 0 mV with and without the MS, respectively). With M3, signal intensity due to the mirror (MS) was 1.4 mV (4.5 mV and 3.1 mV with and without the MS, respectively). Thus, the homodyne gain was estimated to be \(\sim\)8 times. For the estimation, the path difference (\(L_{\text{Obj}}\)-\(L_{\text{Ref}}\)) was set to be zero for each measurement. Note that the homodyne gain depends on the alignment accuracy, sample reflectivity, and path loss. After inserting M3 and scanning the THz frequency over a range from 0.5 THz to 0.7 THz at each pixel, the false contrast was corrected and the detailed shape of the PCB board under the plastic lid was clearly revealed, as shown in Figure 5d. The frequency tuning speed of the DML can be up to 1 THz/30 us [19], which was not fully utilized in this study due to the limited speed of the control electronics. Currently, it requires more than 5 h for a broadband image. By using a broadband optical beating source [20], the imaging speed can be significantly improved, and even in this case, the homodyne gain mechanism can still be effectively utilized. In this interferometric setup, a tomographic image can also be possible in principle by the Fourier transform of the frequency scan results at each pixel. At present, we achieved the depth resolution of 6 mm by scanning over 15 GHz at the center frequency of 0.67 THz (see Figure A6 of Appendix A). In principle, the depth resolution can be improved by increasing the bandwidth, and sub-mm resolution would be possible [9]. In our current setup, increasing the bandwidth did not improve the depth resolution. For further improvements, an accurate phase extraction technique should be adopted.
# Performance Comparison of Deep Learning Models for Damage Identification of Aging Bridges
## 4 Experiment and Results
### Measurement Method  
Loss values in deep learning models are primarily determined through a loss-function, an indicator that evaluates model performance by calculating the difference between the model's predicted outputs and the actual values. Commonly used loss-functions include:  
* Mean Squared Error: Employed in regression problems, it is calculated by squaring the differences between predicted and actual values; these squares are then averaged.
* Cross-Entropy Loss: This function is mainly used in classification problems to measure the differences between the predicted-probability distribution and the actual class labels. Binary cross-entropy is typically used for binary classification, while categorical cross-entropy is suited for multi-class scenarios.
* Log-Likelihood Loss: Often used in generative models, this function seeks to maximize the logarithm of the likelihood of the predicted probabilities matching the actual data.
* Custom loss-function for specific tasks: Such functions are tailored to specific problems. The composite loss-function in Mask R-CNN is an example; it is designed for concurrent object classification, bounding box regression, and mask segmentation.  
Figure 4: SWIN architecture.  
To calculate the loss value, both the input data and the model's output for that data are required. Optimization algorithms, such as gradient-descent, are used to minimize the loss value during the training process. Deep learning frameworks and libraries are equipped with a range of loss-functions and internally calculate and track loss values. In this paper, we compared the loss values generated across six types of bridge damage detection, over the course of 300,000 learning iterations.
## 4 Conclusions  
The conversion of an existing TETRA network operating in the 400 MHz band to a new LTE network operating in the 700 MHz band has been analyzed in a real scenario by means of both simulations and measurements. The latter were used to tune the propagation models employed and, in this sense, the values of the obtained path loss for the different type of environments considered (morphographic correction) are offered for both TETRA and LTE systems. Moreover, the study of special scenarios such as natural parks has also been considered in the analysis.  
The results show that, according to the real scenario considered, the number of sites needed in the LTE network--for a specific quality of service (90% for the whole region and 85% for municipal areas), is a factor of 2.4 higher than for the TETRA network.  
Investigation, D.d.R.C., L.J.-L. and J.V.R.  
This work was supported by the MINECO, Spain under Project TEC2016-78028-C3-2-P.  
The authors declare no conflict of interest.
# Reliable Application Layer Routing Using Decentralized Identifiers
## 4 Design  
### Entities and Interactions  
The proposed system allows end-user _client_ applications to interact with IoT devices using an application layer protocol, such as CoAP group communication [29]. IoT devices can be real devices, virtual devices (e.g., generated by a web of things IoT gateway), and cloud-based systems: the nature of an IoT device does affect our design. IoT devices are associated with one or more URLs, referred to as \(Resource_{URL}\). Similarly, a \(Resource_{URL}\) can be associated with one more IoT devices. IoT devices _advertise_ their \(Resource_{URL}\), and these advertisements are digitally signed. IoT devices are attached directly or indirectly to an _edge_ node, which is identified by an _identifier_.  
Our system assumes that \(Resource_{URL}\) is of the form  
\[schema://domain/path\]  
and considers a network of trusted _registrans_ which are responsible for managing the _domain_ part of \(Resource_{URL}\). Domain _owners_ register their domains with a _registrar_; this process is assumed to be secure in the sense that domains cannot be \"hijacked\" or \"stolen\", i.e., an attacker cannot impersonate a legitimate user to a registrar. We assume that there is a mechanism that allows registrars to verify domain ownership. Similarly, we assume that the public keys of the trusted registrars are well-known. Then, each _owner_ can create an arbitrary number of \(Resource_{URL}\) by appending paths or by creating sub-domains. An _owner_ can _assign_ a \(Resource_{URL}\) to an IoT device, or even _delegate_ it to a third party _controller_.  
Figure 1 illustrates an example of \(Resource_{URL}\) management. In this example, there is a domain called \"/smart-city.iot\". The domain owner generates three \(Resource_{URL}\) identifiers and delegates them to three different _controllers_. Each controller uses the delegated \(Resource_{URL}\) to create new URLs and assign them to the corresponding IoT device.
## 4 Results and Discussion
### Species Distribution of Cr in Soils before and after Stabilization  
Figure 6 shows the changes in the Cr speciation distribution of the untreated and stabilized soils. The primary Cr species for the untreated soil were mainly distributed in the exchangeable content (0.82 mg/g), and the reducible, oxidizable, and residual contents were 0.075 mg/g, 0.074 mg/g, and 0.0026 mg/g, respectively. These results indicated that Cr was more mobile and toxic in the untreated contaminated soil. The Cr speciation of stabilized soil was changed significantly. For the FeSO\({}_{4}\)-stabilized soil, the exchangeable fraction was mainly converted to reducible fraction, which was increased to 0.76 mg/g, when the FeSO\({}_{4}\) dosage was increased from 0% to 5%. For the CaS\({}_{5}\)-treated soil, the exchangeable fraction was mainly converted to oxidizable fraction. The oxidizable fraction of Cr in the CaS\({}_{5}\)-stabilized soil with 3% dosage was 0.40 mg/g higher than that in the FeSO\({}_{4}\)-stabilized soil (see Figure 6), Similarly, the oxidizable fraction of Cr in the CaS\({}_{5}\)-stabilized soil with 5% dosage was 0.61 mg/g higher than that in the FeSO\({}_{4}\)-stabilized soil. These results clearly demonstrate the better chemical stability of Cr in the CaS\({}_{5}\)-stabilized soil compared to that in the FeSO\({}_{4}\) stabilized soil. This distinctive alteration in the Cr speciation of the CaS\({}_{5}\)-stabilized soil, especially the substantial increase in the oxidizable fraction, accounted for the reduced leachability and bioaccessibility of Cr.  
Figure 5: Effect of reductant types and dosage on Cr(VI) content of stabilized soils.  
Figure 6: Effect of reductant types and dosage on Cr speciation distribution.
# A Service-Efficient Proxy Mobile IPv6 Extension for IoT Domain
## 3 Proposed Scheme
### Handover Latency in Non-Location-Based RSS Extensions
#### 3.3.5 Buffering Required in Location-Based RSS Extensions  
Contrary to the schemes that only depend on the RSS measure, the location-aware scheme is based on the location of resources and is not affected by RSS error; hence, the additional buffering is avoided by predicting the accurate handover. The starting point of buffering is when the RSS and location are proper for handover. The pMAG keeps buffering the packets until MN moves from the pMAG domain. Upon disconnection of MN, the buffered packets are transferred to nMAG using the bi-directional tunnel. When MN is attached to nMAG, the buffered packets are delivered to MN. The buffering uses Equation (13) in a location-aware scheme.  
\[\text{B}_{\text{Location-aware}}=\lambda s\cdot\text{E}(s)(\varnothing-\text{t }_{\text{report}}+\text{t}_{\text{L2}}-\text{t}_{\text{mag}}+\text{t}_{\text{ WRS}}+\text{t}_{\text{RS}}) \tag{13}\]  
Buffering in LH-PMIPv6 begins when DeReg-PBU is received from pMAG to LMA. Based on the location of network entities, the RSS error is not affecting the performance of this scheme. Buffering begins after DeReg-PBU and ends when LMA receives the PBU message from nMAG after MN is attached to nMAG. Buffering cost in the LH-PMIPv6 is calculated using Equation (14).  
\[\text{B}_{\text{LH-PMIPv6}}=\lambda s\cdot\text{E}(s)(\varnothing-\text{t}_{ \text{report}}+\text{t}_{\text{L2}}-\text{t}_{\text{mag}}+\text{t}_{\text{WRS}}+ \text{t}_{\text{RS}}+\text{t}_{\text{PBA}}) \tag{14}\]