esa-sceva/satcom-chunk-collection huggingface dataset:
# Sustainable Urban Water Management in China: A Case Study from Guangzhou and Kunming
## 3 Case Study Cities in China
### Concerns Regarding Water Quantity  
Water scarcity, which results in urban pluvial floods, is a frequent occurrence in the cities of Guangzhou and Kunming. Although Guangzhou's drainage system has been built, certain districts continue to be inundated by pluvial floods when it rains excessively. When urban floods occur, they pose a threat to public health, infrastructure, and economic viability, particularly in older towns. For instance, on 22 May 2020, several metro stations and roads were flooded, forcing the closure of several lines, and trapping several ambulances on the road. Additionally, the combined drainage system is only 50% complete, and the drainage system in older municipalities is insufficiently maintained. Additionally, historic roads and structures must be preserved, making it difficult to modify the city's drainage system. Additionally, upstream drainage improvements have resulted in fresh flooding in the downstream areas [39].  
Urban flooding has been a frequent occurrence in Kunming in recent years, occurring virtually every year. On the one hand, both the unique geographical location and the uneven distribution of rainfall will place a major constraint on the urban drainage system. Extreme precipitation occurs regularly in Kunming, and the heavy rainfall is concentrated in the lower elevation area (Figure 1), making drainage problematic during water logging and flooding. On the other hand, when urbanization accelerates, the land structure becomes more hardened, resulting in a rising runoff coefficient, increasing the risk of urban flooding.
# Using Financial Instruments to Transfer the Information Security Risks
## 7 Demonstration: An Example Application
### Information Security Options
#### 7.1.3 Participants and Incentives  
The prospective participants, their motivation and incentives for participation in trading of ISO at ISPM is shown in Table 11.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Fixed Specifications** & **Variable Specifications** \\ \hline Issuer & Information Security Prediction Market \\ \hline Information Security Derivative Type & Information Security Option \\ \hline Contract ID & ISO 123 \\ \hline Underlying Event or Condition & The unit-1 of the company “\(O\)” suffers a (pre-defined type of) cyber-attack and the production at the unit is adversely affected for one or more days on or before 31 December 2015. \\ \hline Trading Start Date and Time & 1 October 2015 00-00-01 CET \\ \hline Trading End Date and Time & 31 December 2015 23-59-59 CET \\ \hline Minimum Investment Required & Amount : 1000 (One Thousand) \\  & Currency : USD \\ \hline Maximum Investment Permitted & Amount : 500,000 (Five Hundred Thousand) \\  & Currency : USD \\ \hline Contract Trading Unit (Lot Size) & One (01) \\ \hline Transaction Fee & 0\% _(Zero for the sake of easy calculation and demonstration)_ \\ \hline Payout Trigger Criteria & Failure of production system, thus affecting the production by at least 30\% at the Unit-1 of company \(O\) for three or more hours during the normal work (production hours) would count as a failure for one full day. \\ \hline Decision Criteria & (i) Press release by the company. \\  & (ii) Company’s reporting to a regulator, such as stock market regulator. \\ \hline Payout (Return) Structure & Fixed; 5100 per contract if the predefined (cyber-attack) \\  & event occurs, 50 otherwise. \\ \hline Pay-Off Horizon & On the day of settlement \\ \hline Settlement Date & If there is no news/report of cyber-attack within the trading period, \\  & then the settlement would be on the fourth business day \\  & after the last trading day. However, if there is any news/report of \\  & the incident within the trading period then the settlement would \\  & take place on the fourth business day from the last trading day or \\  & from the day of production recovery whichever is earlier. \\ \hline Independent Third Party Verification Required & No \\ \hline Eligible Investors/Traders & Only verified \\ \hline Know Your Trader/Investor Required & Yes \\ \hline Transferable Instrument & Yes, to other verified traders only \\ \hline Other Relevant Information & Nil \\ \hline \hline \end{tabular}
\end{table}
Table 11: Participants and Incentives for Information Security Options (ISO).  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Participant** & **Trading Side** & **Incentive/Motivation** \\ \hline Oil Company “\(O\)” & Buy & To hedge the cyber-risk exposure to the unit-1. \\ \hline \hline \end{tabular}
\end{table}
Table 10: ISO Specifications.
# Low-Power Rectennas in Microwave Wireless Power Transmission
## 2 Low-Power Rectifiers
### Matching Network Enhancement
#### 2.2.1 Robust Matching Network Deployment
A brief comparison of the main features of the impedance matching strategies discussed above is summarized in Table 2. Overall, the multi-stage transmission line matching method exhibits good performance but is unsuitable for low-power scenarios due to its large size and the associated insertion loss. In contrast, the other three approaches simplify circuit complexity and minimize insertion loss. Notably, the varactor-assisted matching method and the impedance compression technique are more suitable for low-power rectifier designs while also achieving a broad input power range and bandwidth. Figure 5: Rectifier with automatic input power distribution technique: (**a**) Layout, (**b**) Photograph of fabricated rectifier [50]. Figure 6: Wide-power-range rectifier using low-power varactor: (**a**) Layout, (**b**) Photograph of fabricated prototype [55]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Matching** & **Ref.** & **Power Range** & **Frequency Range** & **Insertion** & **Complexity** \\
**Strategy** & **Ref.** & **Power Range** & **Frequency Range** & **Loss** & **Complexity** \\ \hline Adaptive & [50] & \(-10\) to \(30\) dBm * & \(2.4\) GHz & & \\ Power & [51] & \(10\) dBm & \(1.7\) to \(3.6\) GHz * & & \\ Allocation & [54] & \(-2\) to \(18.3\) dBm @ \(2.45\) GHz ** & & & \\  & & \(3\) to \(19.7\) dBm @ \(5.8\) GHz ** & & & \\  & [55] & \(2.4\) to \(20.9\) dBm * & \(2.1\) GHz & & \\ Varactor- & [56] & \(2.5\) to \(25.5\) dBm ** & \(2.4\) GHz & Low & Moderate \\ Assisted & [57] & \(-21\) to \(-16\) dBm ** & \(915\) MHz & & \\ Multi-Stage & [58] & \(10\) dBm & \(2.0\) to \(3.05\) GHz *** & High & Complex \\ Transmission Line & [59] & \(10\) dBm & \(450\) to \(950\) MHz *** & & \\  & [61] & \(5\) dBm & \(1.6\) to \(2.8\) GHz ** & & \\  & & \(7\) to \(23\) dBm @ \(2.35\) GHz ** & & & \\ Impedance & [62] & \(9\) to \(21\) dBm @ \(5.35\) GHz ** & \(2.35\) / \(5.35\) GHz & Low & Simplest \\ Compression & & [65] & \(-5\) to \(13\) dBm @ \(1.4\)GHz ** & \(1.0\) to \(2.7\) GHz @ & \\  & & & \(6.5\)dBm ** & & \\ \hline \hline \end{tabular}  
* Range for \(|S_{11}|<-10\)dB. ** Range for efficiency \(>50\%\). *** Range for efficiency \(>70\%\).  
\end{table} 
Table 2: Performance comparison of various impedance matching strategies. Figure 7: Photograph of ultrawideband rectifier with extended dynamic power range [65].
## 4 Materials and Results
### Data and Model Implementation
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Samples** & **C1/\%** & **C2/\%** & **C3** & **C4/\%** & **C5/\%** \\ \hline Undisturbed expansive clay & 0.9976 & 0.8410 & 0.9832 & 0.9346 & 0.9089 \\
4\% lime-treated expansive clay & 0.9933 & 0.7849 & 0.9849 & 0.9078 & 0.9372 \\
6\% lime-treated expansive clay & 0.9988 & 0.7937 & 0.9876 & 0.9271 & 0.8413 \\
7\% lime-treated expansive clay & 0.9563 & 0.9181 & 0.8455 & 0.9000 & 0.5214 \\
8\% lime-treated expansive clay & 0.9975 & 0.8322 & 0.9437 & 0.9275 & 0.8266 \\
10\% lime-treated expansive clay & 0.9968 & 0.8361 & 0.9875 & 0.9057 & 0.6070 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Reliability of sample data. Based on the numerical characteristics of all indicators at each grade, 2500 cloud droplets are generated according to Equations (11)-(16) to simulate multi-dimensional connection clouds and intuitionistic connection clouds for each grade. Based on the indicators of liquid limit and plasticity index, the two-dimensional connection cloud and intuitionistic connection cloud were generated for comparative analysis, as shown in Figure 3. The one-dimensional clouds, as shown in Figure 3c-f, are the projections of the two-dimensional cloud in the directions of liquid limit or the plasticity index. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Case** & **Case A** & **Case B** & **Case C** & **Case D** & **Case E** & **Case F** \\ \hline Integrated interval & [0.9311,1] & [0.9183,1] & [0.9075,1] & [0.8318,1] & [0.9044,1] & [0.8668,1] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The integrated membership interval of samples. Figure 3: Two-dimensional cloud maps and their projections for the plasticity index and liquid limit: (**a**,**c**,**e**) connection cloud; (**b**,**d**,**f**) intuitionistic connection cloud. Taking case A as an example by which to illustrate the evaluation procedures, the random number generated from the uniform distribution \(U\) [\(u\), \(1-v\)], weight values, and the corresponding numerical characteristics parameters of each grade and each index value were substituted into the Equation (16), and the intuitionistic connection degrees \(z\)(I) = 0.0000, \(z\)(II) = 0.0204, \(z\)(III) = 0.3543, and \(z\)(IV) = 0.0936 were obtained. According to the principle of maximum membership degree, the shrinkage-swelling grade was specified as III. Since the generation of uniform random numbers is for randomness, considering the concept of the Monte-Carlo simulation [19], step 5 was repeated 500 times to obtain 500 intuitionistic connection degrees, and their mean was taken as the optimal estimation of the overall true value \(\mu\) to reduce the influence of random errors on the classification results.
## 4 Results and Discussion
Furthermore, at high frequencies (800-2000 MHz) the GPR signal has a low signal-to-noise ratio due to the far-field assumption [45]. The far-field approach (placing the antenna above a critical height) has the disadvantage of less energy penetrating into the subsoil and, therefore, a shallow investigation depth. To increase the investigation depth, [PERSON] _et al._[48] recently proposed a near-field modeling approach for 3D electromagnetic wave propagation in planar layered media. In this case, the signal-to-noise ratio at high frequencies can be improved by considering the near-field modeling approach and placing the antenna a few centimeters above the ground without disturbing the structure of the ground surface. Dynamics within the snowpack can be observed in the high frequency range using near-field modeling for GPR measurements. The off-ground GPR system can also be mounted on mobile platforms (e.g., quad or snow mobile) to map the real-time freeze-thaw cycles over a field scale.
# A Combined Method for Localizing Two Overlapping Acoustic Sources Based on Deep Learning
###### Abstract
Under challenging conditions, one proposed solution to improve model performance has been to expand the size of the training dataset [12]. This expansion aims to capture a wide range of possible scenarios, including overlapping acoustic events in time and space, varying source locations within one or more rooms, room impulse responses and dimensions, microphone array placement, varying distances between sound sources and the microphone array center, and different noise and reverberation conditions. However, increasing dataset size to cover these diverse conditions significantly raises computational costs for training. To mitigate the issue of increased computational demands, it becomes necessary to reduce the dataset size while preserving model performance. Achieving this requires omitting some of the previously mentioned variations. From both theoretical and practical perspectives, the most impactful and manageable reduction is in the number of acoustic sources--this parameter has the greatest influence on dataset size. In [16], a novel single-source sound localization model was introduced, offering a spatial resolution of 10\({}^{\circ}\). The problem was formulated as a classification task over discrete source directions. The model utilized a combination of sound intensity (SI) features and GCC-PHAT features as input to a convolutional neural network (CNN). It demonstrated 100% prediction accuracy in closed, reverberant environments, outperforming the existing methods. We propose a novel methodology for efficiently localizing two fully overlapping (100%) active sound sources in the time-frequency domain under challenging acoustic conditions. The approach addresses reverberant, noise-free environments with variable signal-to-signal ratios (SSRs) (2.1-57.4 dB) and limited training data. By integrating sound source separation with the single-source localization model introduced in [16], our method enables robust localization even with constrained training. Table 1 presents a comparative analysis of multi-source sound localization methods, highlighting their respective strengths and limitations while demonstrating the improvements offered by the proposed method.
# AUV-Aided Optical--Acoustic Hybrid Data Collection Based on Deep Reinforcement Learning
## 3 Network Model  
### Network Architecture  
As shown in Figure 1, we consider an AUV-based multi-modal data collection network where the deployed nodes are classified into ordinary nodes \(S=\{s_{1},s_{2},\ldots,s_{M}\}\), cluster heads \(CHs=\{c_{1},c_{2},\ldots,c_{N}\}\) and sink node according to their different functions. The sensor nodes are statically deployed on the seabed using anchor chains, where the locations of the nodes are assumed to be known. The CHs perform intra-cluster data fusion and data compression [27] and then wait for the AUV to arrive and collect the data. In particular, during the network formation phase, all sensor nodes are divided into multiple clusters based on spatial distance, and only one node in each cluster is selected as the CH, while other nodes are used as ordinary nodes for data collection [32]. The AUV performs global data collection around all CH nodes and finally reports the data to the sink node.  
In the multi-modal network, each node is equipped with both UAC and UOC modems for multi-modal communication, and they have the same initial energy, sensing and communication capabilities. Specifically, it includes an acoustic modem for exchanging data at a low transmission rate over a long distance and an optical modem with a relatively short transmission distance and high data rate [33]. Meanwhile, the AUV has similar communication capability to ensure the data transmission [16]. Without loss of generality, we assume that the data arrival rate of sensor node obeys a Poisson random distribution with parameter \(\lambda\). When the AUV visits \(c_{i}\), the CHs package the sampled data block into the packets of length \(B_{i}\) with timestamp \(T_{i}\).
## 3 Results and Discussion
### Method Validation  
Method validation is an important factor in ensuring that both qualitative and quantitative analysis is achieved for the compounds of interest. The method validation for the developed UE and MAE methods was achieved by investigating linearity, accuracy, recoveries, limit of detection LOD, LOQ, sensitivity and selectivity/specificity. The R  
Figure 5: Efficiency of different extraction solvents using MAE (\(n=5\)).  
values for the PAHs ranged from 0.9977-0.9993, which were all greater than the minimum value of 0.99. The recovery test (\(n\) = 5, spiked at 10 \(\mathrm{\SIUnitSymbolMicro g}\)/kg) gave a %recovery of 93.7-121% and 79.6-122% for UE and MAE, respectively, with a %RSD that was less than 10% (Table 1) and thus within the acceptable recovery range of 70-120% [29]. [PERSON] et al. also found that ultrasonic showed a good recovery of greater than 80%, except for with naphthalene [9], while [PERSON] et al. found that most PAHs recovery ranges between 75.5-99.7% using MAE, indicating the validity of the extraction and cleanup procedures [30]  
Student's \(t\)-test showed that there was no significant difference (\(p\) = 0.24) between the recoveries of both UE and MAE (Table S5). The LOD and LOQ were calculated by using signal-to-noise ratios of 3 and 10, which ranged from 0.0250 \(\mathrm{\SIUnitSymbolMicro g}\)/kg to 1.21 \(\mathrm{\SIUnitSymbolMicro g}\)/kg and 0.0800 \(\mathrm{\SIUnitSymbolMicro g}\)/kg to 3.54 \(\mathrm{\SIUnitSymbolMicro g}\)/kg for MAE, and from 0.0840 \(\mathrm{\SIUnitSymbolMicro g}\)/kg to 0.215 \(\mathrm{\SIUnitSymbolMicro g}\)/kg and 0.0190 \(\mathrm{\SIUnitSymbolMicro g}\)/kg to 0.642 \(\mathrm{\SIUnitSymbolMicro g}\)/kg for UE, respectively (Table 1). The LODs for PAHs obtained by [PERSON] et al. for MAE were higher at 0.12-2.40 \(\mathrm{\SIUnitSymbolMicro g}\)/kg than those obtained in this study, while for UE the LODs were lower at 0.002 to 1.87 \(\mathrm{\SIUnitSymbolMicro g}\)/kg, as reported by [PERSON] et al. [30,31]. The \(p\) values obtained (Table S5) for both LODs and LOQs showed no significant difference between their concentrations at 0.18 and 0.10, respectively. The results obtained showed that both microwave and ultrasonic extraction methods can be effectively used, although UE was more sensitive and accurate for most compounds.
## 4 Experiment Setup and Evaluation Metrics
### Evaluation Metrics: AWS Cloud Metrics  
The performance of the IoT-Fog-Cloud architecture can be evaluated using a variety of metrics from the cloud side. Since we chose AWS as our cloud service provider, we used Amazon CloudWatch to evaluate the IoT-Fog-Cloud architecture performance [6; 7; 13; 16; 17]. We used Amazon CloudWatch to evaluate the IoT-Fog-Cloud architecture performance since it collects and evaluates data at the same time and gives the following metric, as shown in Table 2. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Equipment Name** & **Equipment Type** & **Quantity** & **Purpose** \\ \hline DHT11 & Sensor & 3 & Generates temperature degree and humidity degree \\ \hline Raspberry Pi & Version 3 (Model B) & 4 & Provides WiFi service and processing and storage capabilities \\ \hline Micro SD card & 32 GB of storage & 4 & Operating system storage \\ \hline Monitor & HP & 4 & Monitors the experiments \\ \hline Keyboards and mice & HP & 4 & Make it easier to work on a Raspberry Pi \\ \hline Power supply/adapter & CanaKit & 4 & Provides the Raspberry Pi with power \\ \hline HDMI cable & onn & 4 & Provides the connection between the Raspberry Pi and a monitor \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Complete list of the hardware utilized in the IoT–Fog–Cloud architecture. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**AWS Cloud Metrics** & **Descriptions** \\ \hline connectsuccess & This is used to count how many successful connections our fog devices made with the AWS cloud. \\ \hline ping.success & This is used to count how many ping messages our fog devices sent to the AWS cloud in the IoT–Fog–Cloud architecture. \\ \hline publishin.success & This is used to count how many publish requests were processed by the AWS cloud. \\ \hline publishout.success & This is used to count how many publish requests were made by the AWS cloud to the fog devices in the IoT–Fog–Cloud architecture. \\ \hline subscribe.success & This is used to count how many subscribe requests were processed by the AWS cloud. \\ \hline unsubscribe.success & This is used to count how many unsubscribe requests were processed by the AWS cloud. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Descriptions of AWS cloud metrics.
### Contribution and Structure  
The contributions of this paper are as follows:  
1. The algorithm can make the source node find the optimal path to transmit to the destination node, which has the advantages of fast convergence speed and improved communication efficiency.
2. This paper proposes an anti-jamming algorithm based on Dyna-Q. This is the first use of Dyna-Q in anti-jamming communication.  
The rest of this paper is organized as follows: Section 2 presents the system model and problem formulation. In Section 3, we introduce the anti-jamming path selection algorithm based on Dyna-Q. The simulation results and analysis are discussed in Section 4, and concluding remarks are given in Section 5.
## 1 Introduction  
The understandable desire for increasingly efficient machines and components is pushing the materials used to date to their limits. To extend these limits further, new alloy systems are constantly being investigated for their properties and technical applicability. In recent years, a new group of alloys has increasingly become the focus of investigations, the complex concentrated alloys (CCAs). These alloys can exhibit outstanding properties compared to conventionally used alloy systems (e.g., high ductility combined with high strength and storage of liquefied gases) [1]. A better-known part of the complex concentrated alloys are the high entropy alloys. CCAs are multi-principal alloy systems typically consisting of at least four different elements with a near equiatomic composition (between 5 wt.-% and 35 wt.-%).  
Shortly after their detailed description by [PERSON] et al. [2] and [PERSON] et al. [3] in the early 2000s, the interest of emerging additive manufacturing technologies for these alloy systems also started. In 2018, the state of research achieved so far was summarized by [PERSON] et al. [4], others in 2017 [5], and in 2020 [6]. The work done so far shows the possible outstanding properties of this alloy group. Unfortunately, the number of possible combinations and variations in chemical composition for this alloy group is almost infinite, so the work on characterization of the possible properties is just beginning. This is especially true if the process variables during production and any subsequent heat treatments are also taken into account.  
Due to recent developments in the field of additive manufacturing, the combination of novel alloy concepts, such as complex concentrated alloys and additive manufacturing processes, is also becoming the focus of research. For example, studies have already been carried out on the use of CCAs for various powder-based additive manufacturing processes [7; 8; 9] and high throughput processes as well [10]. Besides the powder processes, CCAs are getting in the focus of other additive manufacturing, such as wire and arc additive manufacturing processes as well.