esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussions
### Environmental Effect  
Table 8 presents the detailed performance metrics of all cases about the number of trips by each type of vessel, fuel consumption in metric tons (T), GHG emissions in metric tons (T), and % reduction compared with case C0. The GHG emissions are directly converted from the marine diesel oil consumption using the conversion factors described in Section 4.2. Lowering the voyage speed reduces the fuel consumption significantly up to around 30% (see cases C1 and C5). Moreover, utilizing large vessels more also shows good environmental benefits by reducing GHG emissions by 14% (see cases C2 and C6). Finally, our proposed optimal solutions reduce the GHG emissions up to 39% compared with case C0 (see cases C3 and C7).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & **Vessel** & \multirow{2}{*}{**\#Vessel**} & \multirow{2}{*}{
\begin{tabular}{c} **Speed** \\ **Factor** \\ \end{tabular} } & \multicolumn{3}{c}{**Product Volume**} & \multicolumn{3}{c}{**Costs (USD/Year)**} \\  & & & **LV** & **SV** & **FS** & **BS** & **Produced** & **Transported** & **Total** & **Saving** & **Saving (\%) \({}^{*}\)** & **Rental** & **Operating** \\ \hline C0 & FAFU & 2 & 2 & 1 & 1 & 3275.2 & 3262 & 10,034,800 & - & - & 4,258,330 & 5,776,450 \\ \hline C4 & LVFU & 2 & 2 & 1 & 1 & 3275.2 & 3263 & 9,909,600 & 125,200 & 1.2\% & 4,258,330 & 5,651,270 \\ \hline C1 & FAFU & 2 & 2 & 0.7 & 0.7 & 3275.2 & 3261 & 8,892,090 & 1,142,710 & 11.4\% & 4,258,330 & 4,633,750 \\ \hline C5 & LVFU & 2 & 2 & 0.7 & 0.7 & 3275.2 & 3265 & 8,866,310 & 1,168,490 & 11.6\% & 4,258,330 & 4,607,980 \\ \hline C2 & FAFU & 3 & 0 & 1 & 1 & 3275.2 & 3264 & 8,306,520 & 1,728,280 & 17.2\% & 3,285,000 & 5,021,520 \\ \hline C6 & LVFU & 3 & 0 & 1 & 1 & 3275.2 & 3264 & 8,306,520 & 1,728,280 & 17.2\% & 3,285,000 & 5,021,520 \\ \hline C3 & FAFU & 3 & 0 & 0.7 & 0.7 & 3275.2 & 3264 & 7,342,390 & 2,692,410 & 26.8\% & 3,285,000 & 4,057,390 \\ \hline C7 & LVFU & 3 & 0 & 0.7 & 0.7 & 3275.2 & 3264 & 7,342,390 & 2,692,410 & 26.8\% & 3,285,000 & 4,057,390 \\ \hline \hline \end{tabular}  
*: Saving (%) = 100 \(\times\) (Total cost of C0 \(-\) Total cost of the case)/Total cost of C0.  
\end{table}
Table 7: Comparison of vessel selection rule from the best results of all cases.  
In particular, by applying the LVFU policy in case C4, GHG emissions could be reduced 2% compared with case C0. Even though the large vessel's fuel consumption per trip is greater than a small vessel's, the total number of trips could be reduced by using large vessels more often under the LVFU policy. Accordingly, the total fuel consumption could be decreased. However, as the voyage speeds are lowered in cases C1 and C5, the impact of the LVFU policy on GHG emissions becomes smaller. It may be related to the inference discussed in Section 5.4 that slowing voyage speeds may let most vessels travel slowly somewhere, reduce the vessels idling in the waiting area, and lower the chance of selecting a large vessel from those. From cases C6 and C7, we do not see the impact of the LVFU policy as only large vessels are utilized.
## 2 Materials and Methods
### Model Setup
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Synoptic Station** & **Longitude** & **Latitude** & **Hs (m)** & **Hm (m)** & **D = Hs-Hm (m)** \\ \hline Tehran & 35.68 & 51.32 & 1191 & 1144.43 & 46.63 \\ \hline Kerman & 30.25 & 56.97 & 1754 & 1732.34 & 21.1 \\ \hline Kermanshah & 34.35 & 47.15 & 1318.5 & 1393.34 & \(-\)74.84 \\ \hline Tabriz & 38.08 & 46.28 & 1361 & 1442.42 & \(-\)81.42 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The coordinates of the 4 upper air observation stations, the height of the model grids and their height differences within the stations. Hs and Hm represent the heights (above sea level) of the observation stations and model grids, respectively, and D is their difference. \begin{table}
\begin{tabular}{l} \hline \hline
**Model Setup** \\ \hline WRF Version 3.9.1 \\ \hline Domain 1: 274 \(\times\) 256 grid points and 15 km grid spacing; \\ \hline Domain 2: 391 \(\times\) 343 and 5 km grid spacing. \\ Two-way nesting domains. \\ 39 vertical levels up to the top of 100 hPa. \\ 6 lowest level heights: approximately at 12, 35, 65, 100, 140 and 200 m. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of WRF model setup used for the simulations in this study.
## 7 Experimental Results  
### Indoor Test  
In order to verify the functionality and performance of the developed micro-modem, indoor experiments were conducted in a water tank whose dimensions are 250 cm, 80 cm and 70 cm in length, width and height, respectively, as shown in Figure 10(a). For the sake of convenience, transducers are only submerged in the water medium and the rest of micro-modem is placed on the desk in front of the water tank as shown in Figure 10(b). A transducer and a modem are connected by a direct cable. Also, each modem is connected to a corresponding computer via RS-232 cable for control and monitoring.  
Figure 11 shows the user interface for communication tests. The left window for transmission displays transmitted data and the right window for reception displays whether the transmitted data are received and what the received voltage level is.  
Figure 10: Experimental environment for the tests in a water tank. (**a**) Water tank; (**b**) System setup.  
Figure 12 shows a plot of the output voltage of the receiving transducer \(V_{out}\)_versus_ communication distance \(d\) when the input voltage of the transmitting transducer \(V_{in}\) is set to 10 to 50 volts peak-to-peak.  
Hollow symbol curves represent the analytical results obtained by Equations (1) and (6) whereas filled symbol curves stand for experimental results. As expected, according to the analytical results, \(V_{out}\) is nearly directly proportional to \(V_{in}\) and inversely proportional to \(d\). The experimental results have the same slopes as the analytical results, but the values are a little larger than what are expected.  
Figure 11: User interface for communication tests. (**a**) Transmission; (**b**) Reception.  
Figure 12: Output voltage with respect to distance and input voltage in a water tank.  
This is because there exist strong multipath in the small-sized water tank and reflected signals combined with an original signal which make the received signal strength larger.
# Remote Low-Cost Differential Isolated Probe for Voltage Measurements
## 1 Introduction
The sensor layer includes the physical components that allow information to be transmitted between the different nodes that conform the IoT network or to the central system where the information is processed and displayed to the user. The software layer contains the communication control. Finally, the application layer enables centralised decision making and the processing of the collected data set. Within this last layer, we can find different fields of study, such as solutions based on data collection and cloud computing in a distributed data network [22; 23], machine learning, data analytics [24], and state estimation algorithms for energy management tasks [25]. This last layer can also include a web page, dashboard, or any other interface that allows easy interaction with the user. This work presents the development of a high voltage isolated probe for remote monitoring applications, i.e., it is an IoT or IoE device. As a development intended for unattended remote monitoring, galvanic isolation is an important element to ensure the integrity of the data acquisition and communication devices. Similarly, as is the case in wireless sensor networks (WSN) that perform multipoint measurements, the cost per node, in our case per probe, must be contained, since the price per device increases the cost in the deployment of the IoE multipoint measurement system. All this needs to happen while complying with the minimum measurement specifications of the probe to verify compliance with the European standard EN 50160 [26] low voltage standard, also serving for voltage monitoring for other types of applications such as measurement in smart homes and industries [20; 21]. The main objective is to be able to measure line voltages in a three-phase network whose voltage level is 400 V RMS, 567 V peak. For this reason, the voltage measurement range of the probe is set at \(\pm\)600 V amplitude. The bandwidth requirements are set by the need to measure up to the 25 th harmonic of the mains, according to the European standard EN 50160; for 50 Hz mains, this implies the need to reach a frequency of 1250 Hz, although the design is made for a cut-off frequency of 50 kHz. The remote probe is designed to measure the voltage between any two points in an electrical or electronic circuit safely, with galvanic isolation, which provides isolation between the high voltages and the processing system, the microcontroller. The microcontroller will allow wireless communication between the probe and the data collection device. The article is organised as follows. Section 2 describes the software of the developed remote isolated voltage probe. Section 3 shows the experimental validation tests of the proposed remote probe, both with a direct connection to an oscilloscope and with remote data collection. Section 4 presents a discussion of the results and a comparison with another remote probes in the state of the art. Finally, Section 5 shows the conclusions.
# Target Selection Strategies for Demucs-Based Speech Enhancement
## 6 Results
### Output SIR vs. Input SIR  
The impact of the input SIR on the output SIR can be seen in Figure 8. In this evaluation, only one interference is present, to simplify the visualization of the results.  
As can be seen, the greater the input SIR, the greater the output SIR. In the case of the Demucs-Embed, this target selection strategy is able to improve the average performance  
Figure 8: Output SIR vs. input SIR.  
Figure 7: Output SIR vs. number of interferences.  
of the original Demucs in the cases of very low input SIR (<5 dB), but it is not able to do so otherwise. This indicates that the target selection strategy is indeed able to \"nudge\", albeit slightly, the focus of the speech enhancement towards the source of interest. However, it is not able to outperform it when the source of interest is louder than the interferences (i.e., input SIR \(\geq\) 5 dB), in which cases, the original Demucs is able to provide a better speech enhancement. Additionally, although the average performance is improved in low-input-SIR scenarios, the result variability does increase, which indicates some unpredictability when using Demucs-Embed.  
With regard to Demucs-PhaseBeamform, it provides a better average performance and lower result variability compared to both Demucs and Demucs-Embed throughout all input SIR cases. Moreover, the positive relationship between output SIR and input SIR does not appear to be as strong, indicating that it is more robust against changes in input SIR. This may be because of the beamformer that precedes the Demucs-Denoiser model, which, in a way, is carrying out a type of input SIR normalization that provides this robustness.
# Doppler Shift Tolerance of Typical Pseudorandom Binary Sequences in PMCW Radar
## 4 Comparative Performance Analysis
### Investigated Pseudorandom Binary Sequences
#### 4.1.6 Parameterization Examples for the Investigated Pseudorandom Binary Sequences  
Given the investigated PRBSs, Table 1 lists the considered PRBS lengths \(N_{\text{PRBS}}\) and PRBS usable lengths \(N_{\text{PRBS,usable}}\) in this article. For all investigated PRBSs, lenghts of approximately \(N_{\text{PRBS}}\in\{256,512,1024,2048,4096\}\) have been considered when the PRBS of such length exists. To illustrate the achievable maximum range as a function of \(N_{\text{PRBS,usable}}\), two scenarios are considered. The first is aiming automotive applications, where a sampling rate \(F_{\text{s}}=1\,\text{GHz}\) and a carrier frequency \(f_{\text{c}}=79\,\text{GHz}\) are adopted, which results in the range resolution \(\Delta R=0.15\,\text{m}\). The second application is gesture recognition, in which \(F_{\text{s}}=12.5\,\text{GHz}\) and \(f_{\text{c}}=140\,\text{GHz}\) are considered and \(\Delta R=0.01\,\text{m}\) is achieved. The resulting maximum unambiguous ranges for both scenarios as a function of \(N_{\text{PRBS,usable}}\) are shown in Figure 4. An analysis of this figure based on the \(N_{\text{PRBS}}\) and \(N_{\text{PRBS,usable}}\) pairs listed in Table 1 reveals that ZCZ sequences, Golay A and B sequences without sum of their PACFs, and APASs yield the lowest maximum unambiguous range values, in this order, because they have \(N_{\text{PRBS,usable}}<N_{\text{PRBS}}\).
## 5 Conclusions  
As medical applications grow rapidly, reliable routing is highly desirable to allow medical sensors to accurately deliver data packets to the gateway through multi-hop transmissions. For preventing bottleneck issues in Zigbee's AODV routing services, LBMR is proposed to provide a load balancing, robust and reliable routing service for medical applications. To achieve these goals, LBMR consists of LRC, LEA, and RM. LRC provides a multipath layer routing service towards the gateway for medical applications, and LEA estimates load information for data senders choosing the upper layer next hop with the least network loading. The synergy of LRC and LEA eliminates the bottlenecks and thus provides a load balancing routing service. In addition, RM detects and recovers route failures to reinforce route robustness, and therefore reliability is accomplished. The experiment results demonstrate that LBMR achieves much better load balancing than AODV and AOMDV according to FV values. Based on URNs, LBMR provides more robust routing than AODV and AOMDV. Furthermore, LBMR's PDR is better than AODV and AOMDV, and thus LBMR's data delivery is the most reliable. In conclusion, based on the load balancing design, LBMR provides the most reliable and robust routing service for medical applications compared with the current famous in-use routing solutions, Zigbee' AODV and its improved multipath version, AOMDV.  
This work was sponsored by the National Science Council, Taiwan, under grants: NSC101-2622-E-305-003-CC3.  
**Conflicts of Interest:** The author declares no conflict of interest.  
Figure 9: Packet delivery ratio. (**a**) Grid topology; (**b**) Random topology.
# A Review and Future Directions on the Deterioration Evaluation of Concrete Utility Poles
## 5 Issues for Concrete Utility Pole Maintenance
### Issues for Applying Non-Destructive Testing Methods to Concrete Utility Poles
To apply probabilistic evaluation to concrete utility poles, it is necessary to begin by constructing an analytical model. Models that consider the mechanical failure probability caused by wind loads in wooden utility poles, including stress increases due to pole inclination [64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77], are also applicable to concrete utility poles. However, since the time-dependent deterioration models for wooden utility poles are based on wood decay [87], they cannot be applied to concrete. Therefore, as a future challenge, it is necessary to propose a time-dependent deterioration model tailored to concrete utility poles. Consequently, applying deterioration evaluation based on failure probability to concrete utility poles is unlikely to be realized shortly. Regarding the efficiency of deterioration evaluation through the advancement of external visual inspections and the organization of measurement results using UAVs and AI [35; 36; 78; 79; 80; 81], it is believed that this method can be applied to concrete utility poles if data on the correlation between pole inclination, failure probability, and damage level can be collected. Existing studies have already performed feature analysis and narrowed down necessary parameters to some extent, making them a useful reference for selecting the conditions to focus on during the deterioration evaluation of utility poles. In previous studies, many cases utilized training data from damaged utility poles to identify poles in an unhealthy state. However, correlations with the actual damage state of utility poles and clear replacement thresholds have not been addressed. Therefore, as mentioned earlier, clarifying the damaged state of utility poles and correlating it with collected data are essential for improving evaluation efficiency. Similarly, the challenges are identical for other deterioration evaluation methods using various measurement values, such as density measurements of wooden poles and rebar deterioration evaluations via electromagnetics. The flow of a rational deterioration assessment method for utility poles, as derived from this review, is shown in Figure 12. The evaluation method allows for deterioration assessment by incorporating not only the aspects addressed in previous studies but also insights gained from understanding dynamic characteristics based on experimental results (ISSUE I) and establishing boundary values for the measured vibration characteristics (ISSUE II).
## 5 Solution of the Design and Testing
### Positioning and Construction
Additionally, if there existed a positioning point within a 20 m radius of reference point A, it was conclusively located in the block CN. The advantages of the 120\({}^{\circ}\) distribution method are as follows:  
1. The reference point arrangement boasts minimal density, strategically positioned every 100 m, rendering it ideal for location determination in expansive outdoor settings. In contrast to conventional ZigBee reference points, which are typically spaced at intervals of 10 to 30 m, this approach significantly diminishes the cost associated with reference point deployment. 2. The positioning areas resemble a checkerboard pattern, segmented into areas with dimensions of 50 m in length and width. 3. The positioning blocks, designed as square segmented areas, facilitate the creation of straightforward and convenient practical positioning algorithms. 4. When leveraging ZigBee's mesh network configuration technology and energy-efficient characteristics, the placement locations encounter fewer restrictions in real outdoor environments. Consequently, the entire designated area can be deployed in close approximation to a square. 5. Within the central region of each reference point, such as A, an enclosed circular area with a 20 m radius defines a grouping of positioning blocks, augmenting the likelihood of precise positioning interpretation. Figure 51: The 120\({}^{\circ}\) layout methodology employed for the establishment of a regional division model. F. The data essential for positioning can be readily accessed without the need for intricate calculations. G. A heightened level of efficacy is maintained even in scenarios with limited training point data. \(\bigcirc\) The cons of the 120\({}^{\circ}\) distribution method are as follows: A. The distance between any two blocks may vary from 0 to 100 m, potentially impacting the precision of practical assessments. B. While the 50 m\({}^{2}\) interval design is well suited for extensive villa areas, open-air residences, and communal apartments with expansive surroundings, it may not be optimal for locales characterized by vertical structures, national parks, or environments with diverse and dynamic scenery changes.
# RF Energy Harvesting
## 3 Commercially Available RF Energy Harvesters
### Comparison  
For the solutions provided by e-peas and Powercast, both companies share graphs of the efficiency of their products [10; 16]. Although it is not clearly stated, it seems that efficiency is measured as a ratio of the input power at the energy harvester to its output. Those data allow for the comparison of both products. For the PowerCast P2110B module with an input power of \(-10\) dBm and a signal at 868 MHz, the efficiency is 8%. The maximum efficiency of over 60% is achieved on the 915 MHz band with a power of 3 dBm [20]. Such a difference comes from the fact that, generally, the harvester is tuned to a single frequency. This results in a drop in the efficiency for non-central frequencies.  
The e-peas product has different efficiencies depending on the matching network used in the modules. For an 868 MHz signal with a power of \(-10\) dBm, the efficiency reaches more than 40% for a low-power network and over 20% for a high-power network. Those power networks are specially designed impedance-matching networks. This means that choosing a proper power network for the operating frequency will minimise the energy lost during transfer from the antenna to the harvester module. The AEM30940 module reaches a maximum efficiency of over 60% at 868 MHz and with signal power of 5 dBm [16].  
It is difficult to verify these results without testing the particular devices in a laboratory. Therefore, the above data should be treated as marketing unless verified in a controlled environment.  
Other companies do not share data about the efficiency of their products. However, it can be considered that the maximum efficiency of such solutions is not greater than 60% and depends on the matching network used in the circuit. Furthermore, all presented solutions allow energy harvesting over low distances of up to several meters. What is more, every solution presented here requires a dedicated transmitter that allows energy harvesting from the generated waveform. During the heated debate on electromagnetic noise, such a solution seems to be unacceptable on a large scale. None of the solutions usesRF waves that are already propagating, which are available from systems such as mobile networks, radio broadcasts, TV and WiFi.
In this study, we upscaled grassland AGB from ground-based points to a 5 km spatial resolution using the MODIS NDVI product with a 1 km spatial resolution as a bridge to generate a long-term (1982-2018) grassland AGB product in the TRHR. We have three major objectives: (1) to upscale AGB from ground observations to a 1 km spatial resolution; (2) to upscale AGB from a 1 km to 5 km spatial resolution; and (3) to estimate long-term (1982-2018) grassland AGB of a 5 km spatial resolution in the TRHR.