esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods  
### Material  
Table 1 summarizes the physical properties of the porous materials used. The textile fabrics were made of polyester, and their length and fiber diameter were 15 mm and 20 \(\upmu\)m, respectively. The zeolite was compiled from a crystalline alkali alumino-silicate mineral (1318-02-1, Sigma-Aldrich. Inc., St. Louis, MO, USA). The sizes of the zeolite particles ranged between 3 and 10 \(\upmu\)m, resulting in a typical pore size of approximately 4 Angstroms. The density of the zeolite particles was 2.37 g/cm\({}^{3}\). The coconut shell activated carbon was produced with the pelletized and impregnated activated  
carbons (CDGA, Dongyang Co. Ltd., Cheonan, South Korea). The sizes of the coconut shell activated carbon particlxes were between 5 and 10 \(\upmu\)m, and the density of the coconut shell activated carbon was 1.56 g/cm\({}^{3}\). The epoxy used for adhesive was made of a cold mounting resin and crosslinker (West System, Gougeon Brothers, Inc., Bay City, MI, USA), which can be hardened within 12 h at the hardness of 83 Shore D. The density of hardened epoxy was 1.18 g/cm\({}^{3}\). The temperature rise during the cross-linking did not exceed 50 \({}^{\circ}\)C.  
Figure 1 shows the distributions of the pore sizes and images of the internal pores in the porous materials. Pore sizes ranging between 0.4 and 185 nm were measured using the Brunauer-Emmett-Teller (BET) technique, while pore sizes larger than 185 nm were measured using mercury intrusion porosimetry. The internal microstructure was analyzed from the scanning electron microscopy (SEM) images. As shown in Figure 1a, the textile fibers had cylindrical shapes and were intertwined. The micropores, mesopores, and macropores respectively constituted 7.4%, 2.7%, and 89.9% of the pores in zeolite, indicating that most of the pores in zeolite were larger than 0.05 \(\upmu\)m. As shown in Figure 1b, the internal structure of zeolite consisted of irregular polygons. The micropore, mesopore, and macropore distribution in coconut shell activated carbon were 74.7%, 9.7%, and 15.6%, respectively, indicating that the most common pore size in zeolite was the micropore, which is similar to the range of molecular movement in NOx[14]. In addition, the internal structure of the coconut shell activated carbon had many pores of various diameters (Figure 1c). The micropore, mesopore, and macropore distribution of hardened epoxy were 15.0%, 2.7%, and 85.3%, respectively, indicating that the most common pore size in epoxy was the macropore, which was 5.7 times larger than the molecular movement range of SOx and NOx.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Particle Size (\(\upmu\)m)** & **Density (g/cm\({}^{3}\))** & **Fineness (m\({}^{2}\)/g)** \\ \hline Zeolite & 3–10 & 2.37 & 2.3 \\ Coconut shell & 5–10 & 1.56 & 1130.1 \\ activated carbon & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical properties of porous materials.  
Figure 1: _Cont._
# Time-Sensitive Networking Technologies for Industrial Automation in Wireless Communication Systems
## 3 3 GPP 5G Technology for TSN
### TSN Time Synchronization  
TSN time synchronization is supported by 3 GPP 5 GS, which is an IEEE 802.1 AS time-aware system. Moreover, 5 GS is a 5G grandmaster (GM), where UE, gNB, UPF, NW-TT, and DS-TTs are synchronized, and only TSN translators at the 5 GS edge support IEEE 802.1 AS. Therefore, the following two synchronization systems exist [28]:  
* 5 GS synchronization is used for 5G RAN synchronization [29].
* TSN-domain synchronization follows IEEE 802.1 AS.  
The 5 GS clock is first synchronized by the gNB with the 5G GM. The gNB delivers the time information to the UE by 5 GS signaling, and the DS-TT is synchronized with the 5 GS clock by the UE [29]. UPF/NW-TT receives a 5 GS clock from the gNB through the underlying PTP compatible transport network. As for the TSN clock [30], the NW-TT  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Assistance Information** & **Description** \\ \hline Flow direction & The direction of the TSC flow (uplink or downlink). \\ \hline Periodicity & The time period between the start of two bursts. \\ \hline Burst arrival time & The arrival time of the data burst at either the ingress \\  & interface of the UE (uplink flow direction). \\ \hline \hline \end{tabular}
\end{table}
Table 2: TSCAI [28].  
Figure 3: 5 GS acting as a TSN bridge [28].  
receives a gPTP message from the TSN network where the TSN GM exists, updates the link delay and cumulative rateRatio, and adds the ingress time stamping (TSi) value [24]. The NW-TT delivers the received gPTP message to the UEs belonging to the TSN domain through all the PDU sessions connected to the UPF. Upon receiving the gPTP message, the UE transmits it to the DS-TT, and the DS-TT generates egress time stamping (TSe) and calculates the difference between TSe and TSi values as the residence time within 5 GS for the gPTP message. The DS-TT deletes TSi from the gPTP message and adds the residence time to transmit it. In particular, 3 GPP Release 16 5 GS provides time synchronization supporting multiple TSN working domains using \"domain number\" in the gPTP messages.
# Non-Intrusive Load Disaggregation by Linear Classifier Group Considering Multi-Feature Integration
## 3 Methodology  
### Load Disaggregation Definition  
The definition of load disaggregation can be expressed as follows: Given the mixed signal collected at the power entrance of a house, we need to disaggregate the mixed signal into a series of individual components attributed to specific appliances. The mixed signal consists of individual appliance signals which are switched ON at the given moment. It is necessary to design a Boolean coefficient \(a_{n,m}(k)\), which determines whether the mode \(m\) of appliance \(n\) is ON at the \(k\)th sampling point. Mathematically, the mixed signal obtained can be formulated as a linear combination of some unknown appliance power consumption data, which is shown as Equation (1).  
\[P(k)=\sum\
olimits_{n=1}^{N}\sum\
olimits_{m=1}^{M}a_{n,m}(k)p_{n,m}(k)+e(k) \tag{1}\]  
where, \(P(k)\), \(k\) = 1,2,3,...,\(L\) is the aggregated power signal (\(L\) is the number of samples) and \(p_{n,m}(k)\) denotes the individual power consumption of appliance \(n\) in mode \(m\). \(N\) and \(M\) are the number of appliances and modes, respectively. \(e(k)\) stands for the noise signal and small appliances whose power is very small so that they have little effect on mixed signals, including phone chargers, DVD player and so on. So it can be ignored in load identification. The objective is to decode \(P(k)\) and obtain the status of each appliances by using a set of appliance models in the house.  
In existing research, subjected to some prior information, combination optimization is a common method to solve the Equation (1), which searches the optimum appliance status by minimizing the difference between the actual aggregate power and the sum of disaggregated appliance powers.  
\[a_{n,m}(k)=\underset{a_{n,m}(k)}{\text{argmin}}\big{|}P(k)-\sum\
olimits_{n =1}^{N}\sum\
olimits_{m=1}^{M}a_{n,m}(k)p_{n,m}(k)\big{|} \tag{2}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Types** & **Loads** \\ \hline IL or TL & HVAC (heating, ventilation and air conditioner), electric heat loads (water heater, furnace, oven), electric vehicle charger, washer and dryer, dishwasher, refrigerator \\ \hline UCL & High-power devices \\ \hline \hline \end{tabular}
\end{table}
Table 1: Load space to be monitored.
## 3 Safety Evaluation of the DSM
### Structural Analysis  
The DSM is a reinforced concrete structure with a height of 7155 mm, a width of 9920 mm, and a length of 20,500 mm, and the module structure is installed on the sub-base of the bottom. DSM structural analysis includes static, thermal stress, and seismic analysis, and load conditions for each should be established and used for said analysis. After analyzing each load condition using ANSYS 19.2 [18]--a commercial analysis program --the loads were combined as shown in Table 4, according to ACI 349-01 [16] and NUREG-1536 [9]. The dead load was used for calculation after summing the masses of various structures, and the live load was considered to be the snow load and the load generated on the structure during SNF handling. The wind load was considered to be 60 m/s of wind speed, according to KEPIC [19], and the temperature distribution of the thermal load was derived as a result of thermal evaluation. Seismic load was applied with a maximum acceleration corresponding to 0.3 g, which is a safe shutdown earthquake (SSE) standard. The maximum acceleration was derived using the acceleration time history conforming to the design response spectrum of US NRC RG 1.60 [20]. Accident load was applied as the load generated by the impact of an SNF transport vehicle with a load of 200 tones at a speed of 8 km/h. Figure 12 shows the structural analysis results for load combination 8 with maximum stress in off-normal conditions, while Figure 13 shows the structural analysis results for load combination 16 with maximum stress in accident conditions. The structural analysis results obtained by combining each load are summarized in Table 4. As a result of structural analysis, it was confirmed that the structural integrity was maintained, as the maximum stress was determined to be below the allowable stress for all load combinations.  
Figure 11: Temperature results under accident conditions at the upper wall.
# Routing Strategies for Isochronal-Evolution Random Matching Network
### Model of the Isochronal-Evolution Random Matching Network  
We define a matching network as a network in which no two edges have a vertex in common. Matching is a concept used in graph theory to describe a collection of edges of a graph that are pairwise disjoint [18]. The matching network can be modeled using the classic random graph model, which is described as follows.  
Given \(\Theta\) labeled vertices \(v_{1}\), \(v_{2}\),..., \(v_{\Theta}\), where \(\Theta\) is an even number and \(\Theta>2\), we first choose two vertices among these \(\Theta\) vertices at random to create an edge by connecting them; then, we choose two vertices among the remaining \(\Theta-2\) vertices at random to create another edge by connecting them; we continue this process until \(\theta\) edges have been chosen. Consequently, we generate a matching network with \(\Theta\) vertices and \(\theta\) edges, which is called the Random Matching Network (RMN). If an RMN with \(\Theta\) vertices has \(\frac{1}{2}\Theta\) edges, i.e., all vertices are covered, it is known as a Random Perfect-Matching Network (RPMN).  
In this paper, an RMN with \(\Theta\) vertices and \(\theta\) edges is called \(RMN(\Theta,\theta)\). Obviously, \(RMN(\Theta,\theta)\) is a specific type of Erdos-Renyi random graph with \(\Theta\) vertices and \(\theta\) edges [19]. We define an Isochronal-Evolution Random Matching Network (IERMN) as a dynamic network whose topology is an RMN in each slot. Accordingly, a dynamic network whose topology is an RPMN in each slot is known as a Isochronal-Evolution Random Perfect-Matching Network (IERPMN).
## Example raw logs  
Further, the input data undergo a parsing process. The Drain parser identifies the parameters within each entry and replaces them with a wildcard token \(<*>\), creating log templates. If the resulting template was not observed during model training, the entire entry is replaced with the wildcard token. Otherwise, an unknown log template would spoil the following process of feature extraction. The result of parsing the example data is three log templates: _instruction cache parity error corrected_ had no parameters, so became a template; _generating core.1521_ and _generating core.1521_ had one parameter each and have been aggregated into the same template _generating core.\(<*>\)_; _CE sym 2_, at 0x0b85f2a0, mask 0x04_ and _CE sym 2_, at 0x1b85f4e0, mask 0x05_ had three parameters each and have also been aggregated into the same template _CE sym \(<*>\)_, at \(<*>\), _mask \(<*>\)_.  
To reduce the resources utilized by the model, each template is replaced by its index in the dictionary. The resulting template index sequence is then split into sequences of a specified length, in this case three. The results of splitting the sample input data are two log template sequences: [0, 1, 2] and [1, 2, 0].  
Figure 3: Scheme of detecting anomalies in system log files.  
The next step is to represent each log template (log event) in the sequence in vector form. The fastText model stores a vector representation of each template observed during training, making it possible to convert the template index into a vector from the Euclidean space. The exceptions are log templates that were not present in the training dataset and were aggregated to the wildcard template (\(<*>\)). The fastText model could not learn the context in which such templates occur, so it assigns them a vector filled with zeros. The vector representation of a given sequence of logs is a coordinate matrix consisting of vectors corresponding to each log event. The last step before anomaly detection is to transform the coordinate matrix of each sequence into a vector by averaging the rows. Using the first sequence as an example, the coordinate matrix was averaged to the following vector: [0.625, 1.000, 0.625, 0.875, 0.875].  
Finally, the averaged sequence vector is given as the input to one of the binary classifiers, which, after a supervised learning process on the training dataset, is able to evaluate whether a given sequence vector is anomalous.
## 4 Simulation Results
### Final Remarks  
Detailed analysis discussed in this paper indicates that vehicle detection has been an active research field in recent years. From providing early warning signals and monitoring up to exercising control, there are several examples of major research in intelligent vehicle detection. This paper presented a detailed analysis on vehicle detection on three publicly available dataset. For the task of vehicle detection, YOLO-v5-based architecture was used. To make the YOLO-v5-based architecture more intelligent and flexible, a transfer learning methodology was introduced. Detailed analysis indicated that the proposed approach performed well on challenging datasets.  
In addition, a detailed comparison of the proposed method was carried with fourteen recent state-of-the-art approaches. We are optimistic that this study will be a fair guideline for beginners and practitioners to modify or use any detector for their desired tasks or applications. Below we list the final summary of developed vehicle detection method on three datasets.  
**PKU:** On this dataset, in the G1 category, the proposed method yielded mAP of 99.94%. In the G2 and G3 categories, the proposed method yielded 100% vehicle detection mAP. In the G4 and G5 categories, the proposed method yielded 99.73% and 99.96% mAP, respectively. Overall, on the PKU dataset our method yielded 99.92% vehicle detection accuracy. Out of the fourteen compared methods on the PKU dataset, the proposed method ranked 1 st among all compared approaches.  
**COCO:** On this dataset, with image resolution of 512 \(\times\) 512 pixels, the proposed method yielded 52.31% mAP values and ranked 1 st among all the compared methods.  
**DAWN:** This dataset contains four prominent sub classes, which are fog, rain, snow, and sand. On images that were affected by fog, our proposed method yielded a mAP value of 29.66% and ranked 3 rd out of fourteen compared methods in this category. Meanwhile, for images that contained rain, our developed method produced a mAP value of 41.21% and ranked 1 st along with [31] among all compared works. For images that contained snow, our method yielded a 43.01% mAP value and ranked 2 nd among all compared works. In this class, the work developed in [37] ranked 1 st by yielding a 43.02% mAP value. For images that contained sand, our developed method yielded a 24.13% mAP value and ranked 1 st among all methods. In this class, the work developed in [28] also produced a par result by yielding a mAP value of 24.10%.
# Study on the Driver Visual Workload of Bridge-Tunnel Groups on Mountains Expressways
## 2 Materials and Methods  
### Experimental Scenario  
The definitions of highway bridge groups and tunnel groups are illustrated in Table 1. Briefly, the highway tunnel groups comprise two or more tunnels with a certain spacing, including continuous and adjacent tunnels. The connecting zone of a tunnel group is subjected to abrupt changes in the traffic environment and frequent accidents. Consideringthese changes in lighting conditions and road surface friction when entering and exiting tunnels, drivers struggle to maintain a safe distance and speed between vehicles, resulting in a high risk of rear-end collisions. Frequent changes in the visual environment greatly impact the psychological and physiological qualities of drivers. To ensure the visual needs of drivers in tunnels, the \"Guidelines for Design of Lighting of Highway Tunnels\" (JTGT-D70/2-01-2014 [36]) divides tunnel lighting into threshold, transition, interior, and exit zones. The illumination for tunnel threshold, transition, and exit zones consists of basic and enhanced lighting, and the former is the same as that of the interior zone. In mountainous highway bridge-tunnel sections in China, the speed limit is typically 80 km/h. Accordingly, the recommended lengths for the threshold, transition, and exit zones are 100, 300, and 60 m, respectively. Recommended luminance for threshold, transition, interior, and existing zones are 39-78 cd/m\({}^{2}\), 1.56-11.7 cd/m\({}^{2}\), 1.5-3.5 cd/m\({}^{2}\), and 7.5-12.5 cd/m\({}^{2}\), respectively. Based on the definitions of bridge and tunnel group, this paper adopts the definition of bridge-tunnel group in the \"Guidelines for Design of Lighting of Highway Tunnels\" (JTGT-D70/2-01-2014 [36]) and \"Operating Safety Technology of Freeway Special Section\" [37]. In [PERSON]'s research on the minimum fixation time of drivers in tunnel sections, the maximum lighting adaptation time of tunnel exit was suggested at 12 s [38]. The types of bridge-tunnel groups along mountainous freeways are defined as follows:  
Direct type: The starting and ending points of the bridge are directly connected with the entrance and exit of the tunnel. Indirect type: A road section located between the bridge segment and the tunnel entrance/exit, with a length less than the driving distance within the recommended adaptation time (12 s). The experimental road was the Yakang Expressway in Sichuan, China, which extends from Ya'an City to Kangding City. The Yakang Expressway is a bidirectional four-lane highway with a roadbed width of 24.5 m. Each lane is 3.75 m wide and designed at 80 km/h. The road is located in the transition zone between the Sichuan Basin and the Qinghai-Tibet Plateau, with an elevation ranging from 600 to 2500 m, and a vertical difference of up to 1900 m. Opened to traffic in late 2018, it features 44 tunnels and 129 bridges, with the bridge and tunnel mileage accounting for 82% of the total length. It serves as a typical example of
# An Intelligent Event-Sentiment-Based Daily Foreign Exchange Rate Forecasting System
## 3 Proposed Methodology  
The proposed methodology is explained in Figure 1.  
The proposed system is based on a deep learning model that incorporates the sentiment of major local and global events. The sentiment analysis is carried out by calculating the percentage of positive and negative tweets rather than only considering the overall impact. This way, neutral tweets are neglected, and the sentiment is presented in a much better way. The details of deep learning architecture are presented below.
# Strategies for Scaleable Communication and Coordination in Multi-Agent (UAV) Systems
## 4 Multi-Agent Communication and Networking
### Hierarchical Multi-Level Clustering
A universal implementation of MLC does not exist, but the operations above describe a general architecture [52]. MLC protocols can also serve as reliable underlays for high-rate shortest-path protocols with less reliable links. The underlay delivers the low-bandwidth control packets of the overlay while the overlay delivers the high-bandwidth data packets of the application layer [53].
# Qom--A New Hydroologic Prediction Model Enhanced with Multi-Objective Optimization
## 6 Discussion  
The main goal driving us in this work has been to provide a practical tool for hydrologic prediction based on the combination of two components--the Qom model and its automatic calibration using multi-objective optimization algorithms. The results reported in the previous section indicate that the Qom model is stable for a set of heterogeneous scenarios with different conditions and it does not produce large changes to small variations of the parameters. Therefore, it can be considered an adequate model for its use in the basin hydrological risk estimation, in basin climate change assessments, and in changes by use or soil practices.  
A question that can arise is why have we proposed a new hydrologic model instead of using an existing one? The reason is related to the expertise of the paper authors, comprising hydraulic and computer science engineers. We are the creators of the jMetal software framework for multi-objective optimization metaheuristics. jMetal includes a large amount of algorithms representative of the state of the art but, as pointed out in Section 4.3, choosing which is the most promising one to solve a given optimization problem is not a easy task, in particular for experts in the application domain, which are usually not experts in the optimization techniques.  
In this sense, a solution selection strategy is also suggested for guiding the expert in hydrology in the decision making process, which comprises the use of independent data series for calibration, as well as for validation, each of them involving different time periods of a given problem instance. This leads the proposal to select successful solutions that could be generated by calibration algorithms with low contribution in the Pareto reference front approximation, but with very stable results in terms of practical validation.  
Our starting point was to use jMetal for the calibration of a hydrologic model; although this algorithmic library is implemented in the Java programming language, it hinders using existing models; for example, the Sacramental Soil Moisture Accounting (SAC-SMA) model is available in FORTRAN, Matlab, and R, but to the best of our knowledge there is no a Java version. Consequently, from our experiences in water resources we decided to define a new model--Qom--based on a reduced number of parameters to be adjusted, to implement it in Java, and integrating it into jMetal to use a multi-objective approach for automatic calibration, all aimed at providing a generic and accurate software tool that is freely available to the community.  
Our objective in this paper has not been to compare Qom with existing models, nor to compare the automatic calibration scheme with existing strategies. These issues remain open and are included as lines of future work in the next section.
## 3 Results
### Relationship between Laser Energy and Acoustic Pressure  
The relationship between laser energy and generated acoustic pressure is shown in Figure 3 through a linear curve fitting. The pressure amplitudes at focal points were measured for all the excited laser energy levels in all three transducers of different diameters (2 cm, 5 cm and 8 cm). The increased laser energy (mJ/pulse) increased positive and negative pressures in all CNT-PDMS composite transducers [17], except for the 2 cm diameter transducer, which showed a saturation at \(-\)10 MPa without any further increase in negative pressure with increasing laser energy [10]. Table 2 shows the values for the slopes, intercepts, and R\({}^{2}\) of the linear fitting graphs shown in Figure 3. The R\({}^{2}\) value closer to 1 shows a stronger linear relationship between the two values, except for the negative pressure curve for the 2 cm composite transducer.