esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Samples and Experimental Methods
### Fractal Models  
On the basis of obtaining MIP and LP-N\({}_{2}\)GA data, the fractal characteristics of pore structures are obtained through the Menger sponge model and the Frenkel-Halsey-Hill (FHH) fractal model, respectively. This study established the Menger sponge model for seepage pores based on mercury intrusion data. The procedure for establishing Menger sponge model using MIP data can be found in [33]. For absorption pores, this study established the FHH fractal model based on LP-N\({}_{2}\)GA data of selected samples [47]. The FHH fractal model is described as:  
\[\mathrm{Ln}(\mathrm{V}/\mathrm{V}_{0})=\lambda\mathrm{ln}(\mathrm{ln}(\mathrm{ p}_{0}/\mathrm{p}))+\mathrm{K} \tag{1}\]  
In Equation (1), \(\mathrm{p}\) is the equilibrium pressure (Mpa) and \(\mathrm{p}_{0}\) is the saturated gas pressure. Thus, \(\mathrm{p}/\mathrm{p}_{0}\) represents the relative pressure of the gas. \(\mathrm{V}\) is the adsorbed gas volume under \(\mathrm{p}\), and \(\mathrm{V}0\) is the volume of monolayer molecule adsorbed gas. \(\lambda\) is a power law exponent dependent on fractal dimension (D) and adsorption mechanism. \(\mathrm{K}\) is a constant value.  
According to Equation (1), if the porous material is fractal, there will be a linear relationship between \(\mathrm{ln}\mathrm{V}\) and \(\mathrm{ln}(\mathrm{ln}(\mathrm{p}_{0}/\mathrm{p}))\) with a high linear correlation coefficient. The relationship of the power law exponent \(\lambda\) and the fractal dimension D can be derived from the plot as:  
\[\mathrm{D}=3+\lambda \tag{2}\]  
From Equation (2), the fractal dimension D can be obtained [46].
## 3 SPCA-Based Iterative Solution
### Single User  
We consider Equation (7) in a single-user case; i.e., for \(K=1\). We can rewrite the Equation (7) as  
\[\min_{\tau_{0},P_{A}} P_{A}\] (8a) s.t. \[0\geq 2^{R_{thre}/(1-\tau_{0})}-1-(\alpha_{1}P_{A})/\tau_{0}- \alpha_{1}P_{A} \tag{8b}\] \[0\geq E_{thre}/\tau_{0}-\varsigma_{k}\left|h_{k}\right|^{2}P_{A}\] (8c) \[0\leq\tau_{0}\leq 1\] (8d) \[0\leq P_{A}\leq P_{max} \tag{8e}\]  
This problem is non-convex because of the first constraint, so we need to solve it. Firstly, we convert this non-convex problem to convex sub-problems using sequential parametric convex approximation. After that, the transformed convex problems are solved by the interior point method in a solver such as MATLAB CVX [18, 19]. Thus, we converted the above non-convex problem into a convex sub-problem and solved it using the iterative SPCA method in Algorithm 1. This method was used earlier for the energy-harvesting fairness problem in [8]. We set a new variable \(\widetilde{P}_{A}\), where \(P_{A}=\widetilde{P}_{A}^{2}\). As we know [19], \(\widetilde{P}_{A}^{2}\) and \(\widetilde{\widetilde{P}_{A}^{2}}\) are convex functions with reference to \(\widetilde{P}_{A}\) and \(\tau_{0}\). At the \(n_{t}h\) iteration, we exploit \(\widetilde{P}_{A}^{2}\geq 2\widetilde{P}_{A}^{(n)}\widetilde{P}_{A}- \widetilde{P}_{A}^{(n)^{2}}\) and \(\frac{\widetilde{P}_{A}^{2}}{\tau_{0}}\geq 2\frac{\widetilde{P}_{A}^{(n)} \widetilde{P}_{A}}{\tau_{0}^{(n)}}-\frac{\widetilde{P}_{A}^{(n)}\tau_{0}}{{ \tau_{0}^{(n)}}^{2}}\). Thus, we rewrite Equation (8) into a convex sub-problem as,\[\min_{\tau_{0},\widetilde{P}_{A},s,r} \widetilde{P}_{A}^{2}+s+r\] (9a) s.t. \[0\geq 2^{R_{blue}/(1-\tau_{0})}-1-\alpha_{1}\left[2\frac{ \widetilde{P}_{A}^{(n)}\widetilde{P}_{A}}{\tau_{0}^{(n)}}-\frac{\widetilde{P}_ {A}^{(n)}\tau_{0}}{{\tau_{0}^{(n)}}^{2}}\right]- \tag{9b}\] \[\alpha_{1}\left[2\widetilde{P}_{A}^{(n)}\widetilde{P}_{A}- \widetilde{P}_{A}^{(n)^{2}}\right]-s\] \[0\geq E_{blue}/\tau_{0}-\varsigma_{i}\left|h_{i}\right|^{2}\left[ 2\widetilde{P}_{A}^{(n)}\widetilde{P}_{A}-\widetilde{P}_{A}^{(n)^{2}}\right]-r\] (9c) \[0\leq\tau_{0}\leq 1\] (9d) \[0\leq\widetilde{P}_{A}\leq\sqrt{P_{max}} \tag{9e}\]  
```
1:initialize: find a feasible point for Equation (9) as initial point \(P_{A}^{(0)}\), \(\tau_{0}^{(0)}\), and find \(k^{(0)}=\ P_{A}^{2(0)}+s^{(0)}+r^{(0)}\)
2://initial loop:
3:repeat
4:\(n\gets n+1\)
5: Solve Equation (9) using CVX and find \(P^{*}\), \({\tau_{0}}^{*},k^{*}\)
6: Assign \(k^{(n)}\ \leftarrow\ k^{*}\), \(\widetilde{P}_{A}^{(n)}\leftarrow\widetilde{P}_{A}^{*}\), \({\tau_{0}}^{(n)}\leftarrow\ {\tau_{0}}^{*}\)
7:until
8:\(\frac{[k^{(n)}-k^{(n-1)}]}{k^{(n-1)}}\leq\ tolerance\) and \(s+r\ \leq\ \varepsilon\)
9://main loop:
10: Generate 10,000 samples for \({P^{(n)}}^{*}\), \({\tau_{0}}^{(n)}^{*}\) that satisfy Step 7
```  
**Algorithm 1** Sequential parametric convex approximation (SPCA)-based iterative algorithm to generate data for a single user.  
We generated 10,000 samples for different channel parameters and found optimal values \((P_{A}^{*},\tau_{0}^{*})\).
## 2 Materials and Methods
### Sampling Operations  
Marine water and sediment sampling along the identified transects (see Figure 1) was carried out from December 2019 to February 2020. River water and sediment sampling were carried out in spring 2020. Both sampling campaigns were performed by Hydrolab s.r.l., which fully provided all logistics, from collection to shipping to the analytical laboratory. Marine and river waters were collected using Niskin (or Van Dorn) bottles for a total volume of 5 L. The samples, stored in clean plastic containers, were delivered, shortly after sampling, to the analytical laboratory. The sampling depth is a few meters below the surface for AS samples, a few meters from the bottom for AP samples, and about halfway for the water column for river water samples, avoiding sampling stagnant areas.  
The sediment samples were taken using a Van Veen type bucket and stored in decontaminated plastic containers. Each sample delivered to the laboratory has a mass of 4 kg. The coastal sediment samples (T0-T8) were sampled in an area smaller than 0.5 m\({}^{2}\), up to a maximum depth of 0.2 m.
## 3 Proposed Graph Compression Scheme
### Graph Compression Process  
The compression process in this study, similar to that in the existing studies, involves (i) reading data up to the batch size and window size of the input original graph, (ii) constructing the graph, (iii) finding patterns, and (iv) then representing these patterns with pattern identifiers. Figure 7 illustrates the proposed graph compression process and its structure. As described earlier, each component in the proposed scheme exists in memory, and the actual compression process itself is performed by the compressor. In Figure 7, the compression steps represent the stages in compressing the graph. First, the graph is input as shown, which encompasses the entire process of representing this as a graph. In this stage, similar to the second stage of the compression process, when a graph is constructed and a certain pattern, \(P1\), is formed by \(A-B-C-D\), that part corresponding to the pattern is replaced with \(P1\), and the details regarding that pattern are written together along with the pattern. This process is repeated for each batch to perform the actual compression process. This is represented on the right side of Figure 7. That is, as many compressed graph files are created as there are repeated batches, and the pattern dictionary performs only the task of adding the updated pattern information to one file.  
Algorithm 2 presents the graph compression process. First, the graph for compression is initialized (line 1). Next, the set, _marked_matched_patterns, which indicates and tracks the compression patterns, is initialized (line 2). The _findMatchedPatternsOfPatternInGraph_ function finds the matched patterns, i.e., parts in the existing graph that match each pattern, p, an element of the given pattern dictionary, P (line 5). The matched pattern found in the original graph signifies the part to be expressed as a compressed graph pattern. Afterward, the process of adding elements connected to the original graph and patterns is performed (lines 7-14). Finally, all vertices and edges not included in the pattern are added to the compressed graph. This step is performed to preserve the structure and connectivity of the original graph. The written graph object is then returned and written on a disk to conclude the compression process (lines 16-21).  
Figure 7: Graph compression process and structure.
## 2 Materials and Methods
### Characterization of MWCNTs  
The morphologies of the adsorbents were characterized using a scanning electron microscope (JEOL JSM 6360 LA). Moreover, Brunauer-Emmett-Teller (BET) desorption-adsorption tests were conducted on a BELSORP mini-II, BEL Japan, Inc. The specific external area, mean pore diameter, and pore volume of the MWCNTs were calculated by means of N\({}_{2}\) desorption-adsorption measurements at an N\({}_{2}\) solution with an 89.62 kPa saturated vapor pressure and an adsorption temperature of 77 K.  
Furthermore, FTIR and Raman analysis spectrophotometry were used to measure the influence of the prepared MWCNTs materials--a Shimadzu FTIR-8400 S (Japan) and a Bruker Santerra Raman spectrometer were utilized, respectively. Additionally, the samples were assessed using a particle size analyzer (Beckman Coulter; Miami, FL, USA) at angles of 11.1\({}^{\circ}\) and 90\({}^{\circ}\) to determine the distribution of particle sizes. Additionally, an X-ray diffraction apparatus (D8 Advance X-Ray Di-ractometer; Bruker AXS, Wisconsin, USA) was used to investigate the crystal structure of the McNents with Panalytical in a scope from 10\({}^{\circ}\) to 90\({}^{\circ}\) with K\(\alpha\) Cu radiation.  
\begin{table}
\begin{tabular}{c c} \hline \multicolumn{1}{c}{**Characteristics**} & **Value** \\ \hline Dye name & ISMATE violate 2R \\ \hline Wavelength (\(\lambda\) max) & 550 nm \\ \hline Mol. wt. & 700 \\ \hline Molecular formula & C\({}_{22}\)H\({}_{14}\)N\({}_{4}\)O\({}_{11}\)S\({}_{3}\)CuCl \\ \hline C.I. name & IV2R \\ \hline \end{tabular}
\end{table}
Table 1: Chemical and physical characteristics of the ISMATE violate 2R dye [28].
# Faudistic Platooning: Subdivide Platoons, Unite Networks, and Conquer Efficiency and Reliability
## 5 Results
### Evaluation
When IEEE 802.11p is used, CAMs are broadcasted. For illustration, these messages are received by the virtual broadcast node[14]. In contrast to that, once C-V2X is used, CAMs are sent as multicast and received by the virtual multicast node[13]. Of course, the payload is the same in both networks. Finally, Figure 15 and Table 2 sum up all communication within the feudal platoon. Shares are rounded to two decimal places, which may result in minor discrepancies. During 300 s of simulation, _Feudalistic Platooning_ was responsible for 2684 kB payload data. The share per vehicle varies between 4.77% (128 kB) and 9.95% (267 kB). CAM transmissions are responsible for 57.22% (1536 kB) of the overall amount of data. This amount of data would  
Figure 15: The communication data flow within a platoon performing _Feudalistic Platooning_. also be generated by an ordinary platoon with CAM transmission on two networks. Only 42.78% (1148 kB) are _FeudalMsgs_, used to send or request data to or at the central server, send data to the following vassalage, send data from the central server to the vassalages, and distributing received data within the vassalage. Driving at higher velocity reduces the transmission interval and increases the CAM share even further. For example, if we use the fastest possible transmission of CAMs (0.1 s), then CAM transmissions are responsible for 72.00% of the overall data volume. This shows the limited impact on the data load caused by _Feudalistic Platooning_. Different shares per vehicles are caused by different roles: Lords transmit the highest data volumes, followed by knights and merchants. The existence of preceding and follow-on vassalages is decisive for the knights and the merchants. Should this not be given, the data volume per role may vary: knights that lack a preceding vassalage do not receive its data and thus, do not distribute data among members of their own vassalage. Similarly, merchants that lack a follow-on vassalage do not need to send data from their own vassalage. Both would correspond to the behavior of a peasant. Further to this, we can say that intra-vassalage communication results in 5.8 times more data than inter-vassalage communication. This clearly shows the emphasis of _Feudalistic Platooning_. Furthermore, we observe that only 12.37% (332 kB) of the overall data quantity require cellular infrastructure. As described in Section 4.5, these data are not crucial for the safe operation of the platoon in case of the appropriate selection of an inter-vehicle distance controller. Considering the cellular coverage, especially in mountainous regions and tunnels, it is definitely an important property. Note that we do not consider data transmitted by the underlying platoon management presented in [20]. Small deviations may occur due to the selected start and end of the simulation which may include/exclude single messages.
# Research on Real-Time Control Strategy for HVAC Systems in University Libraries
## 2 Materials and Methods
### LM-UGO Algorithmic
#### 2.2.3 Integration of Algorithm Module with HVAC System  
Temperature, humidity, flow, and other sensors are connected to the algorithmic control module to collect real-time operating data from the HVAC system. The Modbus RTU communication protocol is adopted to ensure that the sensor data can be stably transmitted to the control module. The PID control and optimization algorithm is embedded into the control module, which is used to process the collected data and generate control instructions. The algorithm module analyzes the collected real-time data and calculates the optimal control strategy according to the preset optimization objectives (e.g., the lowest energy consumption and the best thermal comfort). According to the calculation results of the optimization algorithm, it generates control commands for chiller units, cooling towers, pumps, and other equipment such as start-stop and frequency adjustment, and sends the control commands to the controllers of each item of equipment through the communication interface (e.g., RS485, Ethernet). The operating status of each device is fed back to the algorithmic control module for the real-time adjustment of the control strategy, and the algorithmic module dynamically adjusts the control commands according to the feedback data to ensure that the system always operates in the optimal state. Local or remote user interfaces are developed for the manual intervention of the control strategy and for viewing system status and historical data, and remote monitoring and control are achieved through Wi-Fi, the Ethernet, etc., to support real-time viewing and adjustments to system status. The flowchart is shown in Figure 9.
# Estimation of Time-Series Forest Leaf Area Index (LAI) Based on Sentinel-2 and MODIS
## 2 Materials and Methods  
### Study Area  
The study area is located in the Saihanba mechanical forestry field, Chengde City, Hebei Province, China, with the latitude and longitude range of 42\({}^{\circ}\)04\({}^{\prime}\)-42\({}^{\circ}\)36\({}^{\prime}\) N and 116\({}^{\circ}\)52\({}^{\prime}\)-117\({}^{\circ}\)39\({}^{\prime}\) E. The study area is 58.6 km long from north to south and 65.5 km wide from east to west, with a total area of about 934.68 km\({}^{2}\). The area belongs to the southern edge of Inner Mongolia Hunsandak Sandy, where the Inner Mongolia Plateau meets the remainder of the Greater Khingan Mountains and the remainder of the Yinshan Mountains, with an altitude of 1010-1939.9 m. It has a cold-temperate continental monsoon climate, transitioning from semi-humid to semi-arid, with long and cold winters and insignificant summers. It has an extreme maximum temperature of 33.4 \({}^{\circ}\)C, minimum temperature of \(-\)43.3 \({}^{\circ}\)C, average annual temperature of \(-\)1.3 \({}^{\circ}\)C, average annual frost-free period of about 60 days and average annual precipitation of 490 mm. The forest coverage rate reaches 75.5%, the total forest tree accumulation is 4.62 million m\({}^{3}\) and the soil is mainly aeolian sandy soil, meadow soil, brown loam and grey forest soil [67; 68]. The area of artificial afforestation is 573.33 km\({}^{2}\) and natural forest is 160 km\({}^{2}\). The tree species are mainly northern Chinese larch (_Larix principis-ruprechiti_), camphor pine (_Pinus sylvestris_ var. _mongolica_), spruce (_Picea asperata_) and silver birch (_Betula platyphylla_). The location of the study area is shown in Figure 1.
## Materials and Methods
### Adaptive Control Algorithm
#### 2.2.2 Performance Evaluation  
A typical pattern of several setpoints of different length and amplitude also including disturbances of the specific growth rate were selected and modeled to evaluate the behavior of the designed control model. An example of the setpoint profile of the SGR in a simulated process run is shown in Figure 5. The control quality was tested in various operation points of the process by changing the SGR setpoint every hour from 3 to 8 h. These switching points are characterized by different SGR setpoints and accumulated biomass concentrations. Additionally, in order to evaluate the controller's ability to remain stable and compensate disturbances at various process phases, short pump faults were simulated at 4.5, 7.5 and 9 process hours.  
Figure 4: PI controller parameter dependencies generated by the fuzzy model: integration time constant \(T_{i}\) (**a**), controller gain \(K_{c}\) (**b**).  
Figure 3: Integral time absolute error (ITAE) criterion change during each generation of the genetic algorithm.  
The ITAE criterion was selected to evaluate and compare the performance of the developed control algorithm. The adaptive PI controller with fuzzy-based parameter adaptation was compared with the gain scheduling (GS) and model-free adaptive (MFA) algorithms investigated in [19] using the same mathematical model of the biotechnological process (Equations (1)-(9)). The GS method uses additional or already existing online measurements to adapt the controller parameters using the derived functional relationships. The identification of such relationships requires an in-depth analysis of the dynamic properties of the controlled process. The MFA control technique is an alternative data-based approach that does not require deep process knowledge needed for the creation of the process mathematical model. A two-layer neural network with an input layer that has a time-delayed sequence of the tracking errors [19] was used for comparison.
# Device-Free Indoor Activity Recognition System
## 1 Introduction
Thus, human motion creates perturbations in channel state information, and each human activity creates unique perturbations in the raw CSI. Our system can recognize several human activities such as walking, crawling standing, sitting, lying, falling, and empty (i.e., no human) in line-of-sight (LOS) and none-line-of-sight (NLOS) scenarios. Experimentally, we find that each activity has a unique affection on the raw CSI, and, thus, our proposed system can be used in monitoring people in the indoor environments, including detecting their activities. Unlike previous CSI-based human activity recognition approaches, our system detects human activity in LOS and NLOS with high and stable accuracy. Our main contributions are as follows:  
* We present a device-free activity recognition system which enables us to classify several human activities in both LOS and NLOS scenarios. * We present a pattern segmentation algorithm based on local outlier factor (LOF) to detect the abnormal CSI segments that are caused by human activities; then, we extract useful features from both time domain and frequency domain of raw CSI. * We adopt sparse representation classification (SRC) algorithm to recognize the proposed activities, and our classification algorithm gains high-accuracy rates in LOS and NLOS. * We conduct exhaustive experiments with different users that provide us with important insights. Therefore, the system classification ability can be improved by choosing the best parameters gained by experiments. The remainder of the paper is organized as follows. Section 2 presents methodology. Section 3 describes experiment setup and evaluation. Section 4 presents discussion. Section 5 concludes the paper and presents some suggestions for the future work.
# Characteristics of Low-Frequency Acoustic Wave Propagation in Ice-Covered Shallow Water Environment
## 4 Parameter Analysis
### The Influence of Sea Water Depth  
The parameters of the sea water depth sound field are the same as before, only changing the sea water depth h, and the sound source is in the middle of the sea water. Figure 6 shows the propagation loss and contour plots when the sea water depth is 10 m, 50 m, 100 m, and 150 m, respectively.  
Figure 5: Curves of propagation loss in different seafloor parameters.  
It can be seen from Figure 6 that when the water depth is relatively shallow (10 m), a large part of the compressional wave energy in the seawater penetrates into the seafloor and ice layer at a short distance, and only a small part is converted into Scholte surface wave propagation. Other conditions remain unchanged. With the increase of seawater depth, the relative distance between the sound source and the ice layer and the seafloor increases, and the number of collisions between the sound line and the upper interface decreases, which is conducive to the long-distance transmission of sound.
# Publish/Subscribe-Middleware-Based Intelligent Transportation Systems: Applications and Challenges
## 4 Application of Publish/Subscribe Middleware in ITS
### Infrastructure
#### 4.1.3 Road Sensor Network (RSN) Infrastructure
Thus, RSNs are crucial in ITS by enabling real-time traffic monitoring, road condition analysis, and Smart City integration. As a specialized subset of WSNs, RSNs leverage multihop communication architectures to efficiently collect and transmit traffic-related data using a combination of sensor nodes, relay nodes, and base stations. Their hybrid topology ensures scalability and adaptability to dynamic traffic patterns, enhancing urban mobility and road safety. The use of publish/subscribe middleware further optimizes data processing and event-driven communication, as demonstrated by [PERSON] et al. 's [65] parameterized spatio-textual publish/subscribe system, which significantly improves filtering efficiency for high-volume, geo-textual sensor data. However, network infrastructure constraints, energy efficiency, and real-time data processing challenges remain key areas for further optimization. Integrating RSN with edge computing, 5G connectivity, and AI-driven analytics will be essential for advancing autonomous vehicle technologies and intelligent traffic management solutions.
# Overcoming Limitations of LoRa Physical Layer in Image Transmission
## 3 Background
### Comparison of LoRa and Other LPWAN Technologies
#### 3.3.1 LoRa versus NB-IoT  
NB-IoT is a network technology based on the long-term evolution (LTE) standard that offers connectivity in the 200 kHz spectrum. Most current IoT applications adopt this technology because of its good coverage and low cost of deployment. NB-IoT has been specified under 3 GPP release 13 for inclusion within the LTE system.  
The advantages of the NB-IoT standard include the ability to deploy the technology using existing LTE networks, a large coverage area, and a high quality of customer service. Similarly, features of the LoRaWAN standard are a long communication range, and thus, coverage area, long time service of user terminals, low cost of network deployment and user terminals, and a high penetrating power. However, the data rates achievable with LoRaWAN are relatively low [18].  
Despite the large coverage area and high-quality link offered by NB-IoT even when implemented using existing LTE networks, the cost of the deployment of this technology is expensive compared to LoRa, as a greater cost is incurred in upgrading existing base stations [18].