esa-sceva/satcom-chunk-collection huggingface dataset:
# Intermediate Isolation System with Nonlinear Lower Structure and Isolation System
## 6 Time History Analyses on 3D Models and Comparison to IIS Design Spectrum
### Nonlinear THA: Nonlinearity in the Isolation System  
Further analyses on 3D FE models have also been performed by accounting for the nonlinear response of the isolation system, made of HDRBs, as discussed in Section 6.1. Linear behaviour for the lower structure is considered in these analyses, while subsequently nonlinear behaviour of both the isolation system and the lower structure is considered.  
In Figure 9a, the RSA curve is contrasted to the results of nonlinear THA on the 3D FE models only accounting for the nonlinearity in the isolation system. A substantial correspondence of nonlinear analysis results with RSA is found for the cases B and E, while the values of displacement ratio derived from nonlinear THA for the design solutions C and D are slightly lower than the RSA counterparts. Therefore, also accounting for the hysteretic behaviour of the isolation devices, the RSA provides an estimate of the expected behaviour _on the safe side_.
# Experimental Evaluation of Unicast and Multicast CoAP Group Communication
## 5 Evaluation
### Performance Evaluation on a Wireless Sensor Testbed  
We have conducted the majority of the evaluation tests on the w-iLab.t wireless sensor testbed in Zwijnaarde [32]. This testbed provides a controlled test environment in a large (66 m \(\times\) 20 m) open room with 60 fixed nodes and 15 mobile nodes. Each node includes sensors (Rmoni RM090 and Zolertia Z1) and Wi-Fi (IEEE 802.11a/b/g/n). In our experiments, we have used up to 40 fixed nodes as sensor nodes, two fixed nodes as a sensor network GWs and two other fixed nodes to generate interfering Wi-Fi traffic when needed. Figure 6 shows the part of the testbed that we used during our experiments. The other nodes were idle during the experiments to make sure that they do not cause extra interference and are not shown in Figure 6 for clarity. The two GWs are connected via an Ethernet link. When both GWs were used, they operated on different IEEE 802.15.4 Radio Frequency (RF) channels. However, in most experiments, we used only GW1 and kept GW2 idle in order to not interfere with the running experiments. In all of the performance evaluation experiments, we used the Rmoni RM090 boards with Contiki. We have used RPL as the routing protocol and enabled the SMRF multicast engine. We did not use any RDC. The GWs are running the example rpl-border-router provided by Contiki, and therefore, they are the RPL DODAGroots for their subnets, delegate the global IPv6 prefix and route traffic to and from the constrained networks. All other nodes run the Erbium server and also have RPL enabled. In Table 6, we summarize the most important settings for Contiki and the used protocols.  
Figure 6: Experimental setup at w-iLab.t Zwijnaarde: generic wireless testbed. The circles represent the location of the nodes.  
In the following subsections, we present the results of our evaluation experiments on the testbed and compare them with simulated results when appropriate.
# Can We Exploit Machine Learning to Predict Congestion over mmWave 5G Channels?
## 3 Scenarios and Dataset Generation
### Scenario Setup
In turn, the RLC buffer would empty indicating that there is not congestion in the access network. On the other hand, UDP traffic sending is not affected by channel fluctuations, so that it would continue transmitting data leading to a congestion event. We have obtained traces for different distances and data rates, and for each configuration we record parameters from different layers, as follows:  
Figure 1: General description of the scenario under analysis. * Physical layer: although the mmWave physical channel is actually seen as a black-box to the upper layers, we measure its evolution, to understand how its capacity variation translates to upper layers metrics. In particular, we monitor the amount of data sent to the physical channel as the transport-block-size that is actually used in each transmission opportunity. This parameter is relevant to understand how some network configurations, such as the scheduler policy, may impact the overall system behavior, including congestion-related metrics at the transport layer. * MAC layer: in this layer we specifically focus on the RLC protocol. Congestion at the access segment caused by physical channel fluctuations translates into a higher buffer occupancy at the access-stratum protocol stack. Although other buffers exist at the physical layer to implement the HARQ, it is the RLC level where most of the buffering takes place. We monitor the evolution of the RLC buffer length, as an indicator of the level of congestion at the access network. We estimate it as the difference between bytes received at the protocol instance and those that were sent and acknowledged, so that it considers the actual occupancy of transmission and retransmission buffers. In our scenario configuration, RLC buffers are considered to have an infinite capacity, so that RLC SDUs are never dropped. * Transport layer: considering that losses are rather unlikely, congestion is reflected at the transport layer by a delay variation. For that reason, we registered the packet delay as well as the Inter-arrival Time (IaT). In particular, the packet delay is measured as the time between the moment the packet left the application sender until it is successfully received at the destination. On the other hand, the IaT is measured as the time between two consecutive packet receptions. Although these parameters would not be directly accessible from the congestion-control algorithm, both can be easily estimated by means of the Round-Trip Time (RTT) and the time between ACK segments, respectively.
## 3 Results and Analysis
### Data Collection and Processing
The findings indicate that lane change execution time ranges intensively from 3.6 s to 9 s, with a moderately positive correlation between length and speed, as shown in Figure 11. The average lane change execution length, about 160 m, has been entered into the vehicle behavior parameters of the VISSIM model.
## 5 Implementation and Testing Environment
### Experimental Setup  
The experimental setup is summarized in Table 2. The core system components include a long-term support (LTS) version of Ubuntu (Version 18.04.1, Canonical, London, United Kingdom, 2018) as an operating system, with 32 Gb memory, and graphics processing unit (GPU) is GeForce 1080 (Nvidia, Santa Clara, CA, United States). The implementation is done in Python language along with Tensorflow API (Version 1.13, Google Inc., Mountain View, CA, United States, 2019).
## 5 Conclusions  
The following conclusions were drawn from this in vitro study:  
1. Intraoral and model scanners showed similar results in terms of marginal and internal fit, while CBCT demonstrated higher marginal and internal gap values.
2. For the marginal gap, intraoral and model scanners showed clinically acceptable values; however, CBCT exceeded the clinically acceptable limits.  
Although inlay restorations fabricated using CBCT data were not found to be sufficiently accurate in this study, the ability to fabricate them from CBCT data suggests feasibility. Given that CBCT imaging is frequently acquired for diagnostic or surgical planning purposes, exploring its application in inlay restoration fabrication may repurpose existing data and reduce patient visits, clinical steps, and treatment costs--offering a practical alternative in specific clinical situations. Studies aiming to optimize accuracy and validate its clinical applicability are encouraged.  
[PERSON], [PERSON] and [PERSON] conceived and designed the study. [PERSON] completed the preparations. [PERSON] scanned prepared teeth using intraoral and model scanner. [PERSON] performed CBCT scans and converted DICOM data to STL. [PERSON] and [PERSON] performed micro-CT scans and made measurements. [PERSON], [PERSON] and [PERSON] designed inlay restorations and milled using MCXL. [PERSON], [PERSON] and [PERSON] wrote and reviewed the manuscript. [PERSON] edited the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
CBCT Cone Beam Computed Tomography  
Micro-CT Micro-Computed Tomography
## 4 Experiment Verification
### Security Aspect
#### 4.3.2 Prevention of key Generation Information Exposure  
Key generation information should not be known even when a malicious hijacking attack. For this purpose, TargetValue and FrequencyTable were used. The experiment below measured the time of repetitive operation of the hash with increasing permutation. Detailed environment are as shown in the Table 5 below.  
As shown Figure 15, the experimental result took approximately 16,137 s (approximately 4.5 h) on a 13! basis when experimenting with above-average computer performance. In BlockCondition64, malicious attacker must generate a TransactionPool from TargetValue and FrequencyTable within 64 s. Malicious attacker should find out the TargetValue through repetitive hashing for 16! \(\times\) (64!)/(4! \(\times\)16) number of cases. It is impossible to find out the TargetValue within the BlockCondition64, i.e., 64 s. the method proposed in the paper prevent key generation information exposure attacks.  
Figure 14: Experiment result of Section 4.2.4.  
\begin{table}
\begin{tabular}{c c} \hline
**Category** & **Description** \\ \hline CPU & Intel Core i9 8 Core 2.3 Ghz \\ RAM & 16 GB 2400 MHZ DDR4 \\ OS & Mac OS \\ Language & Python 3.6 \\ External lib & -itertools: permutation \\ \hline \end{tabular}
\end{table}
Table 5: System Configuration.
# Latest Advances in the Global Navigation Satellite System--Reflectometry (GNSS-R) Field
## 5 Cryosphere Applications
### Freeze and Thaw State  
The SMAP-reflectometer's radar receiver measurements of GPS were examined in [51] to assess this method's ability to detect the freeze/thaw state of land surfaces. While the study lacked formal calibration, it analyzed the signal-to-noise ratio (SNR) of the SMAP-reflectometer signals and showed that boreal wetlands had a \(\sim\)10 dB seasonal difference in the SNR, which was consistent with simulations from a basic reflectivity model.
# Power Stability Analysis and Evaluation Criteria of Dual-Infeed HVDC with LCC-HVDC and VSC-HVDC
## 5 Simulation Verification  
Generally, the electromechanical transient simulation is preferred to study the stability of actual AC/DC power systems [23; 24]. As the most widely used tool for power system electromechanical transient simulations, PSS/E can meet various analysis needs of users. The performance of the HVDC model in PSS/E has been confirmed in [25].  
Therefore, in order to verify the effectiveness of the above calculation results, based on PSS/E, this paper analyzes the power stability of the hybrid dual-infeed HVDC system shown in Figure 1. LCC-HVDC adopts the CDC6 quasi-steady-state model [26]. The rectifier side is in constant current control mode, and the inverter side is in constant extinction angle control mode. VSC-HVDC is replaced by a constant power load model and a generator model under constant reactive power control mode and constant AC voltage control mode, respectively. By applying a small current increment \(\Delta I_{\rm d1}\) (about 0.01 p.u.) to \(I_{\rm d1}\), the PSI value at the rated operating point can be obtained. Under different receiving-end system strengths, the calculation results of the two methods are compared, as shown in Table 2.  
Figure 9: Curves of PSI versus ESCR for different \(Z_{12}\) under VSC-HVDC constant AC voltage control mode.  
According to Table 2, with the increase of ESCR, the PSI value increases, and the power stability of the HVDC system is better. Taking the VSC-HVDC constant reactive power control mode as an example, when ESCR = 2.18, the PSI value is only 0.12, and the power stability of the HVDC system is very weak; only when ESCR > 3.02 can the HVDC system operate stably with a high stability margin. In addition, the PSI calculation results under the two methods are basically consistent. Therefore, the aforementioned evaluation criteria are valid.
## 1 Introduction
(2020) [22] using two distinct ML techniques (RF and SVM). ML methods were employed by [PERSON] et al. (2021) [23] to calculate the dry matter content needed to classify mangos as mature. In a study published in 2016, [PERSON] et al. [24] used ML algorithms to recognise green citrus fruit. In the work of [PERSON] et al. [25] from 2023, the mass of 20 bean genotypes was estimated using four distinct ML algorithms: SVR, k-NN, MLP, and RF. Apple fruit diseases were researched by [PERSON] et al. (2019) [26], and apple leaf diseases by [PERSON] et al. (2018) [27]. [PERSON] et al. 2019 [28] used four of the most popular supervised learning techniques along with various colour spaces to classify Cape gooseberry fruits. With the help of several predictor variables, the multiple linear regression (MLR) method calculates the predicted variable and generates a regression equation that may be used to predict and explain its value. Even though it can predict complicated non-linear problems better than single predictor variables, it cannot handle complicated non-linear problems. By aggregating the best results from numerous predictor variables, it estimates predicted variables [29]. Lately, the use of MLR has also shown outstanding development in the field of agricultural production. [PERSON] et al. (2018) [30] used MLR algorithms to forecast the sugar content of 'Fuji' apples based on multispectral images. [PERSON] et al. (2021) [31] investigated the use of fruit mineral elements in ANN and MLR to estimate the titratable acid content and soluble solids of loquats. [PERSON] and [PERSON] (2020) [32] built an MLR model to estimate chlorophyll content in pomegranate leaves utilising contact imaging on a smartphone. [PERSON] (2018) [33] conducted research on the modelling of sesame (Sesamum indicumL.) oil content using ANN and MLR approaches. [PERSON] et al. (2017) [34] evaluated the prediction capacities of MLR and ANN in predicting fruit firmness with each nutrient content. To create models for the forecasting of seedless grape quality characteristics, [PERSON] et al. (2022) [2] set out to validate the efficacy of MLR modelling utilising the nutritional status of leaves, within leaf mineral elements, total chlorophyll content, and total carotenoids. The researchers also extensively used ML and MLR models together to estimate tomato plant traits such as mass and volume, chlorophyll and single carotenoids, total soluble solids, disease, Lycopene, firmness, yield, ripening, and bruising [35; 36; 37; 38; 39; 40; 41; 42; 43]. Researchers have successfully and satisfactorily resolved a great deal of difficult issues by using these models. Numerous pieces of research have shown that ML and MLR modelling is a dependable and useful predictive technique for nonlinear data processing, such as fruit properties [31; 34]. In this study, the prediction of total soluble solids content (TSS) of a beef-type tomato cultivar was carried out using ML technique and MLR models. The regression equation was written with the MLR model, the metric results of the models established with both methods were obtained and the models' performances were contrasted.
## 3 Free-Space Distribution  
For free-space clock distribution, because of the much stronger disturbance to the optical beam due to turbulence, there is no need to introduce additional noise. Meanwhile, since air has a much smaller dispersion than fiber, the multiheterodyne bandwidth can potentially be much greater. These two distinctions make the free-space test different from the fiber-optic test.
# Accurate Indoor Localization with IoT Devices and Advanced Fingerprinting Methods
## 4 Discussion  
The results of our study demonstrate significant advancements in indoor localization accuracy using IoT devices and fingerprinting methods. By integrating Bluetooth Low Energy (BLE) and a Radio Signal Strength Indicator (RSSI), we were able to address the limitations of GPS in indoor environments effectively. Our findings corroborate previous research on the potential of BLE for indoor positioning, highlighting the enhanced accuracy achieved through our proposed algorithms.
## 4 Results, Discussion, and Concluding Remarks
### Superiority and Limitations  
The primary strength of this study is the proposal of a novel strength prediction model based on GBDT and PSO for the \(Q_{\text{u}}\) and \(u_{\text{p}}\) prediction of helical anchors in dense fine silica sand. The proposed method was robust in predicting \(Q_{\text{u}}\) and \(u_{\text{p}}\) and the established model provides new insights into the importance of influencing variables. Broadly, the low-cost, less time-consuming, and non-destructive prediction of \(Q_{\text{u}}\) and \(u_{\text{p}}\) in this work will help to promote the utilization of helical anchors in engineering.  
In terms of limitations, the analysis in this work was focused on dense fine silica sand; thus, the lack of results for \(Q_{\text{u}}\) and \(u_{\text{p}}\) in medium-dense sand and loose sand is a clear limitation. In addition, data regarding the influence of soil particle size were not included in the present study. Further experimental studies, which could provide more data, are thus required.
# Non-Thermal and Thermal Physical Procedures--Optimistic Solutions in the Winemaking Industry
## 1 Introduction
The characteristics of the oak chips, such as species, origin, porosity, granularity, permeability, and toasting level, as well as the duration of contact, significantly influence the quality of the final product [18]. Exploring innovative techniques for wine aging has the potential to reduce traditional aging times, making wines available on the market more quickly. This review focuses on synthesizing publications related to the use of some thermal and non-thermal processes in winemaking. Although these optimization techniques are increasingly studied for the food industry, there are limited studies relating to their application to winemaking. Overall, 303 papers were found in the Web of Science Core Collection when searching with the following titles: 'cold plasma in wine' (12 papers), 'ultrasounds in wine' (117 papers),'microwaves in wine' (36 papers), 'cryomaceration in wine' (17 papers), 'high pressure processing in wine'(4 papers), 'pulsed electric field in wine' (117 papers). Figure 2 presents the co-occurrence of the studied subjects. Out of 1150 keywords, 211 meet the threshold of three co-occurrences. It can be observed that ultrasound and pulsed electric field treatments are the most analyzed techniques between those mentioned above, while the cold plasma procedure is less studied. In general, the existing papers are focused on phenolic compound extraction in red wine production, Cabernet Sauvignon, Tempranillo, and Pinot noir wines being predominant (Figure 3). The same set of data was used for all presented figures. According to Figure 3a, when Cabernet Sauvignon and Pinot noir were analyzed, the focus was on the evaluation of the influence of different modern extraction methods (pulsed electric fields, thermovinification, high-power ultrasounds) of phenolic compounds. Regarding the Tempranillo variety (Figure 3b), the extraction of bioactive compounds following the application of pulsed electric fields is predominant. The chromatic characteristics of the samples, correlated with the content of anthocyanin, are the most analyzed parameters. Figure 2: Co-occurrence of the keywords found in the existing research on non-thermal and thermal physical technologies in winemaking. The size of node signifies the number of occurrences, while the distance of the linking lines characterizes the relation between two nodes. The color of the items indicates the cluster to which they belong. Figure 3: Principal particularities of experimental research when Cabernet Sauvignon, Pinot noir (**a**), and Tempranillo (**b**) wines are analyzed. The size of node signifies the number of occurrences, while the distance of the linking lines characterizes the relation between two nodes. The color of the items indicates the cluster to which they belong.
## 3 Performance Parameters
### Bit Rate Coverage  
The bit rate coverage for the FttC VDSL2 network is considered to assess the effectiveness of FttC fronthauling for 5G. It is defined as the probability the bit rate for \(K\) bonded copper pairs at the distributor is greater than a reference bit rate value. This has been evaluated by computer calculations in accordance with the following procedure:  
* We consider one cable from one Cabinet. From the FttC database we first extract the distances of the distributors from the considered Cabinet.
* We evaluate the bit rate for the \(K_{m}\) copper pairs assigned to the \(m\)-th radio node connected at the \(m\)-th distributor at distance \(d_{m}\) from the Cabinet and \(m=1,2,\ldots,n_{D}\) and \(n_{D}\) is the number of distributors along the considered cable. The sum bit rate is obtained using (1) and (5).
* The bit rate calculation has been repeated a number of times. In each trial we have varied the number and the positions of active NTs inside the buildings and connected to the considered cable. This allows to consider variable FEXT conditions influencing the bit rate. In particular, indicating with \(p,0\leq p\leq 1\), the activity of the single VDSL2 user connected to the cable and assuming independence among users, in each trial the number of active users at each distributor along the cable has been generated in accordance with a geometrical distribution with parameter \(p\) over \(M_{m}-K_{m}\) possible active pairs at the distributor and \(M_{m}\) is the total number of copper pairs at the \(m\)-th distributor, \(m=1,2,\ldots,n_{D}\).
* Sum bit rate calculations have been repeated for each cable and for each Cabinet in the FttC database and the calculated DS and US sum bit rates in (5) have been collected to determine the coverage.