esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Centralized Treatment Plants
### Industrial Wastewater Remediation
#### 2.3.8 Jewelry Industry  
The wastewater from the various processes of jewel production is generally corrosive and potentially contains dissolved metals such as gold, silver, rhodium, copper, the dominant heavy metal pollutant, zinc, chrome, nickel, cyanides (CNs) and complexing agents. The integration of electrocoagulation technology, adsorption and wetland treatment was studied as a cheaper and convenient solution to meet the local environmental requirements for industrial WW discharge. The WW (10-12 m\({}^{3}\)/day) was neutralized (pH 6-8) and pumped into a 1.5 m\({}^{3}\) EC unit, equipped with an aluminum plate as the anode and an iron plate as the cathode. Silica sand and granular activated carbon were used as adsorbents. The wetland (14 m\({}^{3}\)) performed the biosorption step with tropical plants and gravel media. COD was the key parameter of performance, and the analyses of TSS, cyanide (CN), copper and oil and grease were also applied. This technology was able to reduce TSS (71%), COD (91%), Cu (95%), CN (96%) and oil and grease (71%). The highest COD removal occurred in the electrocoagulation unit (69%), wetland (39%) and adsorption (33%) [110].
## 4 Experiments
### Limitation  
GPNet indeed encounters a challenge when dealing with small datasets. To investigate this issue, we conduct a series of experiments on the CASIA and NIST16 datasets, and the results are displayed in Figure 5. We observe an overall upward trend in forgery localization performance as the number of training images increases. The AUC metric shows significant improvement, reaching its peak when training images reach 300. These findings indicate that the performance of GPNet benefits from a larger number of training samples.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Distortion** & **F1** & **AUC** \\ \hline No distortion & 91.2 & 99.7 \\ \hline Resize (0.78\(\times\)) & 90.8 & 96.5 \\ Resize (0.25\(\times\)) & 87.7 & 92.4 \\ \hline GaussianBlur (\(\kappa\) = 3) & 88.4 & 96.8 \\ GaussianBlur (\(\kappa\) = 7) & 85.1 & 90.1 \\ \hline JPEGCompress (\(\eta\) = 100) & 90.8 & 98.4 \\ JPEGCompress (\(\eta\) = 80) & 90.2 & 98.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Manipulationlocalization performance (AUC%) on NIST dataset under various distortions.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Variants** & \multicolumn{2}{c}{**NIST16**} & \multicolumn{2}{c}{**CASIA**} \\  & **F1** & **AUC** & **F1** & **AUC** \\ \hline CNN Branch & 83.7 & 96.0 & 49.8 & 80.3 \\ Transformer Branch & 74.3 & 86.5 & 49.3 & 76.7 \\ w/o TcFusion & 86.8 & 94.3 & 57.5 & 83.3 \\ Ours & 91.2 & 99.7 & 61.4 & 88.4 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation results on CASIA and NIST datasets, AUC and F1 (%) are reported.
## 5 Conclusions  
The embedded GCDRAM is one of the prospective candidates for the realization of the CIM scheme because of its compatibility with the logic CMOS process and compact bit area. In this study, we proposed a computational GCDRAM architecture called DC-GCDRAM, which supports a range of Boolean logic and FA operations for off-load computations and computes XAC operations for binarized neural networks while operating as a normal DRAM for general-purpose workloads. To validate the effectiveness of the proposed DC-GCDRAM, a 4096-word \(\times\) 8-bit CIM macro using logic-compatible 3T dynamic cell, which can be configurable to memory and a range of computing operations, has been implemented with a 45-nanometer generic CMOS technology. For VDD = 1.0 V, the proposed architecture performs memory, logic, and FA operations at 241, 229, and 224 MHz with energy figures of 7.92, 8.09, and 8.19 pJ/cycle. Compared with conventional digital computing, the DC-GCDRAM saves energy and latency of the FA operation by at least 47% and 46%, respectively. At the same supply voltage, the proposed CIM design takes 3.42 ns and consumes 20.8 pJ for two 128-input XAC operations, reaching 24.6 TOPS/W. The results indicate that the proposed architecture achieves an at least 11.9\(\times\) higher energy efficiency as well as an at least 461\(\times\) better energy-delay product compared to the traditional 8-bit-wide computing hardware.  
Conceptualization, Y.C.; methodology, Y.C.; investigation, T.K. and Y.C.; validation, T.K.; writing--original draft, T.K.; writing--review and editing, Y.C.; supervision, Y.C.; project administration, Y.C.; funding acquisition, Y.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2023-00242945) and the BK21 FOUR Program funded by the Ministry of Education of Korea (419990113966).  
Not applicable.  
The data presented in this study are available in the article.  
The EDA tool was supported by the IC Design Education Center in Korea.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & [54] & [55] & [56] & [57] & [61] & **This Work** \\ \hline Supported functions & MAC & MAC & MAC & MAV & DRAM & DRAM \\  & & MAC & MAC & Arithmetic & AzAC \\ \hline Computing method & Analog & Analog & Analog & Analog & Digital & Digital \\ \hline Operating voltage & 0.85–1.1 V & 0.5/0.7 V & N/A & 1.0 V & 0.9–1.2 V & 0.8–1.1 V \\ \hline Nominal frequency (MHz) & 105 & 200 & 250 & N/A & 800 (@1V) & 229 [Logic] \\
[Operation] & [MAC] & [MAC] & [MAC] & [DRAM/MAC] & 224 [Arithmetic] & 224 [XAC] \\ \hline Throughput (GOPS) & N/A & 48 & N/A & 22–691 & 200 [2] & 150 [3] \\ \hline Energy efficiency & & & & & & \\ (TOPS/W) & 102.2–216.8 & 17.8–552.5 & 81.5–115 & 7.4–236 & 8–19.7 [2] & 24.6 [3],(4) \\ \hline Accuracy (\%) & 90.6/91.2 & 82.8/96.78 [5] & 91.52 & 92.02/99.84 & N/A & 100 [5] \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._
# Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives
## 2 Historical Review
### Characteristics of Research Output
#### 2.1.4 The Study Time of Day and Season  
There are strong biases in the study time of day and season of the selected SUHI literature. The majority of the research (69%) concentrated on the SUHI at a single time, especially the daytime (63%), while only 30% of the studies explored the SUHI during the day and night (Figure 3a). Furthermore, the data acquisition times of sun-synchronous satellites usually do not coincide with the time of day where the LST is at a minimum or maximum. With regard to the season, over half of the literature (55%) focused on the SUHI in an individual season, particularly summer (33%), and only 23% of the studies investigated the SUHI in all seasons (Figure 3b). Most of the remaining studies (18%) examined the SUHI in multiple seasons or used multi-season LST data due to data limitation. A few studies (4%) focused on annual mean SUHI or did not provide information on the season examined.  
Figure 2: Geographic distribution of the selected SUHI literature based on the study location. (**a**) The number of studies per city (empty cycle) and country (background color), (**b–d**) the enlarged view of the SUHI publications in North America, Europe, and Asia, and (**e–i**) the number of publications in each continent. One study may cover a series of cities distributed in different countries and/or continents. Two global-scale studies [2,50] were not included in this summary since they did not specify the studied cities. Three regional-scale studies [3,5,51] were not counted in the number of publications in each city (panels a to d) because they focused on the SUHIs of all cities in spite of city size. TP represents the total publications in each continent.  
These temporal biases of SUHI research can be attributed to data limitations, and varying magnitudes of SUHI over time. On one hand, the most widely used satellite for SUHI detection (i.e., Landsat) only has daytime data. Additionally, thermal infrared images are strongly influenced by weather conditions, especially cloud cover, which result in a significant missing-data problem. More details on data limitations can be found in Section 6 below. On the other hand, the SUHI together with its adverse effects (e.g., heat stress) have been widely found to be most intense during the day in the summertime [4, 6, 52]. Therefore, this attracts greater research interest. However, several studies have confirmed that SUHI intensity and the underlying mechanisms vary dramatically through time [4, 6, 53], which highlights the necessity of more comprehensive evaluations on SUHIs across different times of day and seasons. For example, the LST spatiotemporal fusion schemes hold great potential to address this gap [54].
# Predicting Putting Development Using Machine Learning Methods Based on RIOCHTrack Data
## 1 Introduction
Moreover, the test data can be conveniently and efficiently collected and has good integrity. The United States has the largest number of test loops, including the AASHTO test road, the Minnesota test road, Westrack, and the track at the National Center for Asphalt Technology, from which a large amount of data to support research on asphalt pavement service performance and pavement structure design can be collected. In addition, France, Spain, and Japan have their own full-scale pavement accelerated loading loops. Foreign asphalt pavement design systems, pavement structure types, pavement material properties, traffic loads, and natural environments, among other factors, differ considerably from domestic ones, hence, some research outcomes and conclusions based on foreign tests are not suitable for Chinese conditions and road construction characteristics; the design code thus has certain limitations in simulating the service performance of various pavement structures throughout their life cycles. The first full-scale pavement experimental ring track in China, funded and constructed by the Ministry of Transport (Research Institute of Highway Ministry of Transport Track, is located at the road traffic test site of the Ministry of Transport, Majuqiao Town, Tongzhou District, Beijing. Its construction was completed in mid-November 2015, and loading officially began in November 2016. It comprises various pavement structures and materials commonly used in different regions of China and provides technical support for new-generation pavement structure development and material design as well as long-life pavement construction [23]. So far, a large amount of reliable field data has been monitored and collected, including load, climate, pavement structural response, and long-term service performance data on asphalt pavement. At present, there are few studies on rutting prediction using data from RIOHTRack. [PERSON] [24] proposed a modified MEPDG rutting by introducing a growth coefficient based on RIOHTRack data. However, the model needs to be calibrated before use, and the calibrating process requires many parameters. Ref. [25] [PERSON] proposed multivariate transfer entropy and graphy neural networks to predict rutting. However, the input variables are very limited, only including ESAL, temperature, rutting, and variables related to deflection, without considering material properties, and pavement structure. Ref. [26] established ANN coupled with a genetic algorithm to predict rutting. However, only the effects of temperature, ESAL, and center point deflection on rutting were considered. This study aims to establish an accurate and effective rutting prediction model for the rutting development law of commonly used domestic pavements and analyze the key factors affecting rutting changes using on-site detection data from RIGHTrack. The findings can provide a reference for making pavement maintenance decisions.
## 2 Materials and Methods
Cloud types were classified using CTP and the COT calculated from COMS according to the method specified by the International Satellite Cloud Climatology Project (ISCCP) [42]. Using this method, cloud types were classified according to height (low-level: 1000-680 hPa, mid-level: 680-440 hPa, high-level: 440-50 hPa) and optical thickness (thin: 0-3.6, middle: 3.6-23, thick: 23-379). In this study, the COMS cloud products used for the determination of seedable clouds with uncertainties were calculated as follows: Cloud detection using infrared channel brightness temperature (BT) was estimated using the split window and threshold methods, and the probability of detection (POD) was found to be above 88% compared with moderate resolution imaging spectroradiometer (MODIS) data [43]. POD is the rate at which clouds are detected as clouds or clear sky (N\({}_{\text{cloud}}\)/(N\({}_{\text{cloud}}\) + N\({}_{\text{clear}}\)) \(\times\) 100(%)). The cloud phase was estimated via the threshold method using the infrared channel BT of water vapor, which showed an average POD of 80% based on the MODIS cloud phase [44]. Additionally, in accordance with the reflectivity of the 0.6-\(\mu\)m channel, the COT was calculated using a simulated look-up table method depending on the different atmospheric conditions. The root mean square errors (RMSE), obtained on the basis of MODIS COT, were about 3 and 5 in the water and ice phases, respectively [45]. For optically thick clouds (COT \(>\) 10), CTT and CTP were estimated using BT\({}_{10.8}\), whereas for optically thin clouds, they were estimated using ratio of BT\({}_{10.8}\) and BT\({}_{6.7}\). The uncertainty of CTT and CTP was 3 K and 50 hPa, respectively [31; 46]. Based on regression method using the infrared channel BT, RI was calculated, and in accordance with SSM/I, it showed RMSE below 1 mm/h [47]. The abovementioned verification values also included artificial errors resulting from the matching of the spatiotemporal resolution of COMS and MODIS data for verification [48; 49; 50]. Because of the nature of passive sensors in geostationary satellites, low-level atmospheric information of clouds observed by the satellite cannot be determined. As a result, lower-level clouds with a top height exceeding 4 km were not detected. [PERSON] et al. [51] conducted a multilayer cloud detection study using MODIS data. They found that on an average, mid-latitudes generated about 11% of multilayered clouds, and that the occurrence frequency of multilayered clouds on land was higher than that on the sea, i.e., 12% and 10%, respectively. Therefore, in this study, the occurrence frequency of seedable clouds was analyzed by detecting clouds with limited height. However, it can be assumed that, in practice, the occurrence frequency of seedable clouds is higher or similar than the occurrence frequency observed in this study even given the uncertainty shown above. Additionally, the average CTH of seedable clouds detected under limited CTH (\(\sim\)4 km) may be lower than seedable clouds detected under unlimited CTH. Similarly, CTT may be higher.
## 3 Results and Discussion
### Peak Interfacial Friction Coefficient
#### 3.2.1 Relationship between Surface Hardness and Peak Interfacial Friction Coefficient  
Figure 7 shows the relationship between surface hardness and peak interfacial friction coefficient for fine, medium, and coarse sands. Figure 7 shows that the peak interfacial friction coefficient decreased with increasing surface hardness for different particle sizes. The relationship curve between the peak interfacial friction coefficient and surface hardness changed from nonlinear to linear with increasing particle size, which was similar to the trend for peak interfacial shear stress reported in a previous paper.
# Stalling in Queuing Systems with Heterogeneous Channels
## 3 Queuing Characteristics and Optimization  
### Queueing Characteristics  
Probabilities (6)-(8) enable us to calculate the steady-state characteristics of multi-channel QS with stalling as probabilities of various QS states and numbers of queries in these states. Hence, one has at first to calculate the following, according to Lemmas A1 and A2: the \((m+K+1)\times(n+1)\) matrix of auxiliary functions \(A_{i,j}\); \(n+1\) components vector h, used to find the steady-state probabilities according to Theorem A1; and the other characteristics. Note, the obtained explicit expressions are numerically stable; in addition, their complexity does not depend on the number of states, and is only linear with respect to the number of fast channels and stalling buffer length. Some characteristics are given in Table 1, using the following notation for simplicity:  
\[p_{2}=\frac{p_{1}}{(1-q)+p_{1}},\]  
\[C_{2}=\sum_{s=1}^{K}s\cdot q^{s}k\frac{q-(K+1)\cdot q^{K+1}+K\cdot q^{K+2}}{(1 -q)^{2}}\;if\;q\
eq 1,\;\text{otherwise}\;\;C_{2}=\frac{K\cdot(K+1)}{2}.\]  
\[S_{2}=\sum_{s=1}^{M}s\cdot\rho^{s}=\frac{\rho-(M+1)\cdot\rho^{M+1}+M\cdot\rho^ {M+2}}{(1-\rho)^{2}}\;\;\;if\;\rho\
eq 1,\;\;\;\text{otherwise}\;\;\;S_{2}= \frac{M\cdot(M+1)}{2} \tag{11}\]  
A key characteristic of QS is the occupancy probability of all channels and stalling buffer only \(P_{m,n,K}\), derived according to Theorem A1. For calculating the characteristics using (6)-(10), the well-known Newton binomial formula is applied:  
\[\sum_{i=0}^{u}\frac{u!}{i!\cdot(u-i)!}q^{i}\cdot(1-q)^{u-i}=1. \tag{12}\]  
The explicitly given characteristics help us to study various effects; for instance, the effect of a query becoming stuck can be studied--the situation when the fast channels and the stalling buffer are free, but the slow channels are busy by service of queries that arrived before, etc.
## 4 Application to the L'Aquila 2009 Mw6.1 Seismic Sequence
### Dataset Description  
Here, we used a very detailed earthquake catalog computed by [31] by processing continuous seismic data recorded at a very dense seismic network of 60 stations operating for 9 months after the Mw 6.1 main shock. The complete catalog is composed of about 64,000 precisely located foreshocks and aftershocks, spanning from January to December 2009. It was processed by combining an accurate automatic picking procedure for P- and S-waves [36], together with cross-correlation and double-difference location methods [37], reaching a completeness magnitude (i.e., the minimum magnitude above which all of the events occurring in a study area can be reliably detected and located [38]) equal to 0.7, which corresponds to a source dimension of \(\sim\)50 m based on a circular crack model using a 3 MPa stress drop. The earthquake relative location errors are in the range of a few meters to tens of meters (i.e., the error ellipsoids obtained at the 95% confidence level for 200 bootstrap samples show median values of the distribution along the major/minor horizontal and vertical directions of 24, 15 and 27 m; see [39] for details). These errors are comparable to the spatial dimension of the earthquakes themselves, allowing us to image the internal structure of the fault zone down to the tens of meters scale.  
For this work, we use the complete large-scale catalog, relative to the L'Aquila Mw6.1 main shock, differently from what is shown in [35] where the authors choose earthquakes occurring within 6 km (\(\pm\)3 km) from the L'Aquila fault plane, modeled by inverting strong motion, GPS and DInSAR data. That plane is 20 km long in the N133\({}^{\circ}\)E striking direction and dips 54\({}^{\circ}\) to the SW, intersecting the main shock hypocenter. Their resulting catalog is made of \(\sim\)19k foreshocks and aftershocks. Further details on this catalog are described in [32], which deals with the accurate description of the L'Aquila 2009 fault zone structure and damage zone and its implication for earthquake rupture evolution. Our large-scale and complete earthquake catalog represents a perfect test-case to verify the ability of the HYDE algorithm to retrieve both the large- and small-scale complexities of the activated faults.
## 3 Methodology
### Feature Extraction  
The time-domain feature represents the change of bearings vibration signal with the passage of time. Although the time-domain features of bearings vibration signal cannot provide enough degradation information to predict the bearings RUL, it describes the attenuation trend of bearings and reveals whether the running state of bearings is stable. For example, according to the amplitude change of vibration signal and other time-domain features, the bearing can be peliminarily judged whether damage occurs, but different time domain features have different ability to characterize the health status of bearing operation, so it is particularly important to select appropriate time-domain features in bearing running condition monitoring. Frequency variation can display the noise information of bearing vibration signal, and the influence of noise signal can be eliminated by processing the frequency domain features of bearings vibration signal. Therefore, the time and frequency domain characteristics of bearings running signal are selected as the initial research object.
# Performance Analysis of Software-Defined Networks to Mitigate Private VLAN Attacks
## 2 Private VLAN  
Private VLAN (PVLAN) is a technique that can be used to segment and adapt a broadcast domain. PVLANs are used to isolate endpoints at layer 2, so direct communication among endpoints belonging to the same IP network is not possible. PVLANs are configured on switches by assigning ports to one of the following three roles: promiscuous, community, or isolated. Firstly, endpoints connected to a promiscuous port can communicate at layer 2 with endpoints connected to promiscuous, community, and isolated ports. Secondly, endpoints connected to a community port can only communicate at layer 2 with endpoints connected to promiscuous ports and ports belonging to the same community. Thirdly, endpoints connected to an isolated port can only communicate at layer 2 with endpoints connected to promiscuous ports. An example of a configuration of a PVLAN is shown in Figure 1, where there are two promiscuous ports, two different communities, and three isolated ports. Note that trunk links allowing for extending the scope of the PVLAN across several switches are omitted for the sake of clarity.  
As observed in the figure, _P1_ and _P2_ are promiscuous ports. They can communicate with all the community and isolated ports and also with each other. The network gateway is usually connected to a promiscuous port. _C1_ and _C2_ are two different communities composed of six and four ports, respectively. Communications involving ports within the same community, as well as with promiscuous ports, are allowed, but ports in _C1_ cannot communicate with those in _C2_ and vice versa. Finally, the three isolated ports (_I1_, _I2_, and _I3_) can communicate with _P1_ and _P2_ exclusively, since communication with another type of port, even between two isolated ports, is not allowed. It should be noted that isolated, community, and promiscuous ports cannot communicate with default ports of the switch, that is, ports with no PVLAN configuration (the remaining nine ports in the figure).  
A simplified variant of PVLAN is known as PVLAN edge. PVLAN edge uses only two types of ports: protected and unprotected. The ports of the switch are configured as unprotected by default. In contrast, protected ports must be explicitly configured. Communication at layer 2 between endpoints connected to protected ports is not allowed. An example of a PVLAN edge is shown in Figure 2. As can be seen, there are eight endpoints connected to protected ports, and the gateway is connected to an unprotected port. According to this configuration, network users can only communicate with the gateway.