esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Analysis of the Pilot and Data Component of a CSS Signal for LEO-PNT +
## 3 Signal Processing and Design  
In this section, we show the receiver architecture and processing as well as the signal design of the proposed CSS-based PNT signal. Specifically, Section 3.1 deals with the pilot component and the design of the chirp period, whereas Section 3.2 deals with the data component and data transmission capabilities. Before going into detail, it is important to describe the receiving process of a CSS signal as given in (1), that is, the de-chirp process. The de-chirp process is illustrated in Figure 1, and it consists of mixing the received signal with the corresponding local replica and looking for the frequency of the maximum value of the resulting fast Fourier transform (FFT). This process is useful to receive a CSS signal and to estimate the data symbol when the signal is synchronized. Furthermore, this process is the cornerstone block for the design of both the pilot component used for ranging and synchronization [4, 11].
## 2 Study Area and Data Sources
### Data
#### 2.2.3 NEP Data  
The NEP data used in this study is the global daily NEP simulation dataset for the period from 1981 to 2019, published by [PERSON] et al. [35]. This dataset is generated by driving the mechanistic ecosystem model BEPS using remote sensing data of vegetation parameters, meteorological data, and atmospheric CO\({}_{2}\) concentration. It provides high spatial resolution (0.072727\({}^{\circ}\)) and daily temporal resolution. The dataset is widely regarded as highly reliable [36].
## 4 Simulation Test of the Grouser Effect
### Determination of the Significant Factors of the Grouser Effect  
To further determine the degree of the influence of the grouser's parameters on the traction force in the crawler robot's walking mechanism and based on the analysis and selection of the simulation experiment results and the need to determine the optimal performance of the grouser's parameters, a model of the process of the simulated experimental system was constructed. To further determine the significant factors affecting the traction force of the crawler robot, a cause-and-effect was is drawn based on Equations (9)-(14), as shown in Figure 8.  
Through an analysis of Figure 8, it can be seen that the vertical thrust \(F_{1}\) acting on the surface of the track shoe, the force \(F_{2}\) acting on the bottom of the grouser, the force \(F_{3}\) acting on both sides of the track shoe and the grouser, and the force \(F_{4}\) acting on the grouser and the bulldozing resistance \(R\) collectively affects the hitch pull. Through an in-depth analysis, it was found that the parameters related to the crawler such as \(b\), \(l\), \(W\), \(h\), and \(\lambda\), and soil parameters such as \(k_{c}\), \(k_{\varphi}\), \(\gamma\), and \(N_{\varphi}\) have a major impact on the traction force. Among these, the moving speed of the robot and the track's parameters are controllable factors, but the soil parameters are uncontrollable factors. The traction process model in Figure 9 was obtained.  
Figure 8: Causal analysis diagram of the traction force.
###### Abstract  
This paper presents JADEDO, a hybrid optimization method that merges the dandelion optimizer's (DO) dispersal-inspired stages with JADE's (adaptive differential evolution) dynamic mutation and crossover operators. By integrating these complementary mechanisms, JADEDO effectively balances global exploration and local exploitation for both unimodal and multimodal search spaces. Extensive benchmarking against classical and cutting-edge metaheuristics on the IEEE CEC2022 functions--encompassing unimodal, multimodal, and hybrid landscapes--demonstrates that JADEDO achieves highly competitive results in terms of solution accuracy, convergence speed, and robustness. Statistical analysis using Wilcoxon sum-rank tests further underscores JADEDO's consistent advantage over several established optimizers, reflecting its proficiency in navigating complex, high-dimensional problems. To validate its real-world applicability, JADEDO was also evaluated on three engineering design problems (pressure vessel, spring, and speed reducer). Notably, it achieved top-tier or near-optimal designs in constrained, high-stakes environments. Moreover, to demonstrate suitability for security-oriented tasks, JADEDO was applied to an attack-response optimization scenario, efficiently identifying cost-effective, low-risk countermeasures under stringent time constraints. These collective findings highlight JADEDO as a robust, flexible, and high-performing framework capable of tackling both benchmark-oriented and practical optimization challenges.  
Keywords:exploration; optimization; attack-response; security; engineering design; algorithms +
Footnote †: journal: _Algorithms_
## 4 Results and Discussion
### Sample Testing and Analysis
Additionally, scalar measurements of common absorption features in the shortwave infrared range, such as absorption feature position (Pos) and depth (Dep), can be used to characterize their metallogenic environment, chemical properties, and crystallinity. On the other hand, mid-infrared thermal radiation detects molecular vibration fundamentals, which can effectively identify non-hydroxyl silicates (e.g., quartz, feldspar, garnet), carbonates, and sulfates [53]. After spectral data from the SWIR and TIR intervals of the samples were obtained, mineral identification was conducted using TSG. The results (Figure 11) indicate that the main alteration minerals in the region are from the chlorite group (chlorite-Fe, chlorite-Mg, chlorite), mica group (muscovite, biotite, mica), carbonate group (siderite, dolomite, calcite), as well as montmorillonite, amphibole, quartz, and feldspar (sodium feldspar, microlcine) (Figure 9a,b). Since the samples were generally collected from the ore body and its vicinity, the analysis results provide certain indications for the distribution of the ore body. After the combination of the geochemical element analysis results, which show a strong positive correlation between elements such as Mg, Al, Si, S, K, Ca, Mn, and Fe, it can be inferred that the widespread chloritization (with chlorite mineral formula (Mg,Fe,Al)\({}_{6}\)(Si,Al)\({}_{4}\)O\({}_{10}\)(OH)\({}_{8}\)) and carbonation (mainly dolomite with mineral formula CaMg(CO\({}_{3}\))\({}_{2}\)) alteration features in this region are closely related to metallogenic processes. These findings provide effective theoretical evidence for alteration factors in subsequent 3D metallogenic predictions. Elements such as Mg, Al, Si, Ca, and others that show a strong positive correlation with Fe can serve as indicator elements to guide the next stage of deep mineral exploration in this area.
## 5 Results and Discussion
### Rationality of Operating the Multireservoir System
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Interval**} & \multicolumn{4}{c}{**Outflow (m\({}^{3}\)/s)**} & \multicolumn{4}{c}{**Hydraulic Head (m)**} & \multicolumn{4}{c}{**Power Output (MW)**} & \multicolumn{2}{c}{**Total Power (MW)**} \\ \cline{2-13}  & **HJD** & **DF** & **SFY** & **WJD** & **HJD** & **DF** & **SFY** & **WJD** & **HJD** & **DF** & **SFY** & **WJD** & **-** \\ \hline
1 & 118.6 & 277.9 & 389.4 & 100.2 & 121.4 & 102.3 & 69 & 112.4 & 120.4 & 235.3 & 226.3 & 90.4 & 725.7 \\
2 & 0.0 & 351.4 & 592.3 & 675.2 & 132.5 & 114.22 & 69 & 122.2 & 0.0 & 332.4 & 344.2 & 690.4 & 1367.10 \\
3 & 0.20 & 612.2 & 793.5 & 1084.2 & 145.7 & 125.46 & 69 & 130.5 & 0.5 & 652.4 & 463.7 & 1161.3 & 2277.9 \\
4 & 111.0 & 254.2 & 312.2 & 333.1 & 161.3 & 172.23 & 69 & 125.2 & 151.2 & 275.8 & 181.3 & 367.7 & 976.40 \\
5 & 201.2 & 406.9 & 447.6 & 511.4 & 155.2 & 126.2 & 69 & 132.2 & 275.34 & 434.9 & 259.3 & 552.3 & 1521.84 \\
6 & 107.4 & 195.3 & 209.3 & 224.1 & 161.2 & 127.31 & 69 & 134.7 & 145.21 & 211.2 & 121.3 & 244.66 & 722.37 \\
7 & 145.21 & 222.3 & 243.1 & 277.11 & 157.4 & 125.0 & 69 & 134.2 & 200.22 & 221.7 & 139.1 & 305.4 & 866.40 \\
8 & 205.54 & 251.1 & 265.2 & 303.3 & 154.22 & 129.12 & 69 & 132.2 & 261.31 & 261.4 & 156.2 & 331.22 & 1010.13 \\
9 & 227.11 & 265.3 & 276.2 & 275.2 & 145.21 & 127.12 & 69 & 129.4 & 275.31 & 286.3 & 154.2 & 308.14 & 1023.95 \\
10 & 242.65 & 281.2 & 289.1 & 322.2 & 137.6 & 124.12 & 69 & 126.2 & 290.21 & 301.1 & 166.3 & 350.12 & 1107.73 \\
11 & 301.11 & 331.1 & 337.2 & 351.1 & 122.1 & 125.4 & 69 & 124.2 & 302.12 & 355.6 & 197.3 & 380.23 & 1238.23 \\
12 & 60.28 & 293.2 & 302.1 & 841.2 & 124.2 & 122.4 & 69 & 116.3 & 65.21 & 278.2 & 172.3 & 808.6 & 1331.60 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Outflow, hydraulic head, and power output, optimized by CA, for the studied reservoirs.
# A Multisensor Approach to Satellite Monitoring of Trends in Lake Area, Water Level, and Volume
## 3 Results  
### Lake Surface Area  
Both the nFrac and eFrac processes yielded time series of lake surface area from MODIS, from February 2000 through August 2017. These were merged with coarser-resolution AVHRR data, and interpolated to a daily timestep. As discussed in Section 2.2, comparison of the residuals suggested that eFrac had a lower noise level than nFrac--after removal of all MODIS day 361 images and three other outliers, the standard deviation of the normalized residuals was 0.20 km\({}^{2}\)/km for nFrac and 0.12 km\({}^{2}\)/km for eFrac. Thus, the eFrac version was used as the definitive lake area dataset.  
Figure 8 shows the time series of lake surface area for all six individual lakes, along with 206 validation points (black dots) obtained from comparison of the MODIS and AVHRR data to higher-resolution Landsat images (194 during the MODIS era and 12 during the AVHRR era). The lake system as a whole reached a maximum area on or around 1 December 2001, at 1722 km\({}^{2}\). Individual lakes reached their maxima on different dates (Table 4).  
Using the 206 Landsat-derived lake surface area measurements as \"truth\", the mean absolute error (MAE) in the AVHRR/MODIS eFrac time series was 18.7% of lake area, but this was dominated by dates on which lake areas were very small--excluding those with lake areas below 5 km\({}^{2}\) (n = 42), the MAE for remaining dates (n = 164) was 4.8% of the lake area. A scatterplot of lake area estimates from Landsat vs from AVHRR/MODIS is shown in Figure 9.  
Figure 7: Ice, Cloud, and Land Elevation Satellite-1 (ICESat-1) Geoscience Laser Altimeter System (GLAS) laser altimeter orbit tracks (colored lines), March 2003–March 2009.
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 2 Metal Sulfide Thin Films
### Copper Indium Disulfide Films  
The copper-rich thin films were deposited onto soda lime glass by using CuSO\({}_{4}\), InCl\({}_{3}\), and CH\({}_{4}\)N\({}_{2}\)S solutions. [PERSON] and co-workers [75] reported that CuS reacted with In\({}_{2}\)S\({}_{3}\) to produce CuInS\({}_{2}\) (CIS) films during the experiment. The depositions of films took place on glass substrates by using InCl\({}_{3}\), Na\({}_{2}\)S, and CuCl\({}_{2}\) solutions at room temperature [76]. XPS studies confirmed the stoichiometry of CuInS\({}_{2}\). The direct band gap (1.5 eV) and chalcopyrite phase could be observed in the optical absorption spectrum and XRD pattern, respectively. The tetragonal films were prepared as described by [PERSON] and co-workers [77]. The photovoltaic parameter, such as the fill factor (0.31), power conversion efficiency (0.92%), Voc (0.35 V), and Jsc (8.49 mA\(\cdot\)cm\({}^{-2}\)) were investigated in the CIS-sensitized solar cells. [PERSON] and co-workers [78] noted that the annealed films (200 \({}^{\circ}\)C, 1.5 h) were slightly sulfur-rich and showed well-crystalized conditions. The influence of the [Cu]/[In] ratio on the physical properties of the annealed films (argon condition, 500 \({}^{\circ}\)C, 60 min) was studied by [PERSON] co-workers [79]. They concluded that resistivity and stoichiometry strongly depended on the [Cu]/[In] ratio during the experiment. The glass was used as the substrate during the synthesis of films (by using CuCl\({}_{2}\), InCl\({}_{3}\), and Na\({}_{2}\)S) at pH 5, as described by [PERSON] and workers [80] in Equations (25)-(27). The immersion and rinsing times were 30 and 50 s, respectively.  
\[\mathrm{CuCl_{2}+Na_{2}S}\rightarrow\mathrm{CuS+2 NaCl} \tag{25}\]  
\[\mathrm{2 InCl_{3}+3 Na_{2}S}\rightarrow\mathrm{6 NaCl+In_{2}S_{3}} \tag{26}\]  
\[\mathrm{2 CuS+In_{2}S_{3}}\rightarrow\mathrm{S+2 CuInS_{2}} \tag{27}\]
## 1 Introduction
### Review of State-of-the-Art Detection Methods
#### 1.2.4 Time-Frequency and Denoising-Based Methods  
Recent works have explored time-frequency representations (e.g., spectrograms or wavelet transforms) for target detection as they offer better visualization of nonstationary or low SNR signals [7]. Additionally, singular value decomposition (SVD) and Hankel matrix-based models have been used to isolate signal components from noise [8]. These methods exploit the low-rank nature of target signals but may require careful tuning and may not directly yield peak positions.
# ADLBiLSTM: A Semantic Generation Algorithm for Multi-Grammar Network Access Control Policies
## 3 The Semantic Generation Algorithm
### Self-Attention Layer  
At the bottom, we apply the self-attention mechanism to fuse the relevance of words into the original input. The self-attention mechanism is at the bottom of the network and processes the raw inputs directly, weighting the raw inputs as the output of the next layer. A rule, after word embedding, can be represented as \(X^{*}=[a_{1},a_{2},\ldots,a_{n}]\), where \(a_{n}\) denotes the \(n^{\text{th}}\) word in the rule. The input vectors are dotted with the coefficient matrices  
Figure 1: Network structure.  
\(w_{q}\), \(w_{k}\), and \(w_{v}\) to obtain \(Q=[q_{1},q_{2}\ldots q_{n}]\), \(K=[k_{1},k_{2}\ldots k_{n}]\), and \(V=[v_{1},v_{2}\ldots v_{n}]\), as in Equations (1)-(3).  
\[q_{n} =w_{q}\cdot a_{n} \tag{1}\] \[k_{n} =w_{k}\cdot a_{n}\] (2) \[v_{n} =w_{v}\cdot a_{n} \tag{3}\]  
The \(q_{n}\) values of each input word are dotted with the \(k_{n}^{T}\) values of the other input words (including itself) to obtain the attention scores so that the correlation between the words can be obtained, and then the attention scores are normalized using the softmax to form the attention score matrix. Then, each \(v_{n}\) is multiplied by the attention score matrix to obtain its weight vector, and then all the vectors are weighted and summed to obtain the final output \(X=[x_{1},x_{2}\ldots x_{n}]\). This weighted processing method makes the vital information more fully used, which improves the effect of semantic generation. Equation (4) describes the entire process, where \(d_{k}\) denotes the dimensionality of the word vectors, divided by \(\sqrt{d_{k}}\) to prevent computational overflow caused by too large a value of \(QK^{T}\).  
\[Attention(Q,K,V)=softmax(\frac{QK^{T}}{\sqrt{d_{k}}})V \tag{4}\]  
The self-attention mechanism considers the relationship between each element and other elements, processing sequential data in a disorderly manner to better understand the context. In rules such as those for routers and firewalls, the same address may have different labels due to factors such as location and specific connecting words. The self-attention mechanism can ignore non-critical parts of a rule and find the word with the highest correlation to the obfuscated word for judgment. In addition, security policies from the same vendor may adjust the original syntax due to changes in versions or operational requirements. Therefore, employing the self-attention mechanism to capture the relevance of each word in each rule can solve the problem of label inconsistency and syntactic heterogeneity in the semantic generation of security policies.  
However, the self-attention mechanism does not directly include positional information, thus requiring additional mechanisms to capture sequential information.
## 8 Limitations and Future Research  
Remote labs are submerged in a digital world where latency is important. Latency is a technological term, and it is the time any action or operation needs to be executed. Low latency means higher speed and low execution time. A low latency would be beneficial when putting remote labs in operation. It is also crucial to have low latency, considering there will be a few hundred users, and the data could be vast. That is why it is considered that the system's latency be studied to improve performance and user interaction before the remote lab is released to a larger community, whether it is the same faculty, institution, or schools elsewhere.  
In addition, the survey revealed that students found the proposal to introduce remote laboratories into their semester classes both interesting and viable. They recommended incorporating more labs focused on microcontrollers, Programmable Logic Controllers (PLCs), sensors, actuators, and peripherals such as buttons, switches, and LEDs. To meet this demand, plans are underway to establish new laboratories that will cover the practical aspects of various subjects, starting with a lab designed for remote interaction with a Collaborative Robot (COBOT).  
On the other hand, important observations regarding the UAZ Labs platform interface need immediate attention. Efforts will be made to enhance the user experience by making the interface more intuitive, ensuring smoother navigation, providing instant feedback on user actions, improving image quality, and offering access to technical support. Furthermore, functionality will be improved to allow students to easily add, delete, or modify their data and manage appointments more efficiently.  
This remote lab is in an early phase of the project. The purpose was to evaluate the feasibility of remote labs and recommend user improvements before deploying them for students outside the Autonomous University of Zacatecas. The next phases include more improvements to the overall system (functionality, interface, accessibility, democratization, among others).
## 2 Materials and Methods  
### Lubricant Properties  
A hydrocarbon-mimicking RTIL with the chemical formula [(\(n\)-C\({}_{6}\)H\({}_{13}\))\({}_{3}\)(\(n\)-C\({}_{14}\)H\({}_{29}\))P]-[(CH\({}_{3}\))\({}_{3}\)SiC\({}_{2}\)H\({}_{4}\)SO\({}_{3}\)]--designated as P-SiSO--was used as the base fluid in these experiments. P-SiSO was synthesized by Nisshinbo Holdings Inc. (Tokyo, Japan), and the molecular design of P-SiSO has been described in Reference [24]. A low vapor pressure fluid (Fomblin Y 317950)--designated as PFPE--was supplied by Sigma-Aldrich (Stockholm, Sweden) and used as the reference lubricant.  
The linear formula of PFPE is CF\({}_{3}\)O[-CF(CF\({}_{3}\))CF\({}_{2}\)O-]\({}_{x}\)(-CF\({}_{2}\)O-)\({}_{y}\)CF\({}_{3}\), and its molecular weight is 3300 g/mol.  
Five different additives were used, and are referred to as OA, PNA, DMOP, BHT, and OPP, and their respective detailed description is provided below. Oleylamine (OA) (80-90% C-18, primary amines >98%), was supplied by Fisher Scientific (Goteborg, Sweden); and N-Phenyl-1-naphthylamine (PNA) (reagent grade, 98%), 5,5-dimethyl-1,3,2-dioxphosphorhian-2-one (DMOP) (96%), and 2,6-di-_tert_-butyl-4-methylphenol (BHT) (>99%) were supplied by Sigma-Aldrich (Stockholm, Sweden). _Ortho_-phenylene phosphate (OPP) was prepared as described in Reference [25]. These five additives were used to prepare seven lubricant samples with P-SiSO as the base fluid: five single additive samples, and two samples where OPP was combined with either of the amines (OA, PNA), as synergistic effects between OPP and OA have previously been reported in Reference [25]. In all samples, the additives were added at a concentration of 10 mmol/kg. The chemical structures of the ionic liquid base fluid and the five additives are depicted in Figure 1. Density and viscosity properties can be found in the Supplementary Information (SI).
# A Review of Passive Constant Flow Regulators for Microfluidic Applications
## 2 Passive Constant Flow Regulators--General Considerations
### Working Fluid  
Except for the device developed by [PERSON] et al. [13], almost all devices considered in this review were designed to work with water, cerebrospinal fluid (CSF), or other Newtonian fluids like viscous mixes of water and glycerol. In principle, the constant passive flow regulators are also compatible with gas. The modeling of a gas flow regulator can still be based on the standard continuum approach, which is still valid if no-slip boundary conditions apply, typically for Knudsen number \(Kn<10^{-2}\) (viscous flow), where \(Kn\) is defined as the ratio of the fluid mean free path and the macroscopic length scale of the physical system [14].  
In the laminar regime, a change of temperature induces a change of fluid viscosity which, in turn, leads to flow rate variability as for standard flow restrictors. Since the hydraulic resistance is proportional to the fluid viscosity \(\eta\), the relative flow rate error is thus proportional to the opposite of the relative viscosity change with temperature, considering that the reference temperature is \(20\ ^{\circ}\)C:  
\[Q(T)\propto\ \frac{1}{\eta(T)}\rightarrow\ \frac{Q(T)-Q(20^{\circ}\mathrm{C})}{Q( 20^{\circ}\mathrm{C})}=\ -\frac{\eta_{T}-\eta_{20^{\circ}\mathrm{C}}}{\eta_{T}}=\frac{\eta_{20^{ \circ}\mathrm{C}}}{\eta_{T}}-1\,. \tag{4}\]  
Table 1 shows, at selected temperatures, the relative flow rate error due to viscosity change. The values are derived from the empirical formula proposed by [PERSON] et al. for the dynamic viscosity of water in the range \(-8\) to \(+150\ ^{\circ}\mathrm{C}\)[15]. Relative flow rate errors vary from \(-34\%\) to \(+53\%\) for operating conditions in the range \([+5;\ +40]\ ^{\circ}\mathrm{C}\). It is noteworthy that additional error sources shall be considered to determine the passive flow regulator accuracy (e.g., fluctuations in the flow regulation stage, hysteresis).
# mmS-TCP: Scalable TCP for Improving Throughput and Fairness in 5G mmWave Networks
## 2 Related Work  
### Characteristics of 5G MmWave Channel  
Figure 1 shows a 5G network scenario in which user equipment (UE) communicates with a remote server through a base station (BS in the figure). The BS, which provides mmWave wireless service to mobile devices, communicates with the remote server over an Ethernet connection.  
In the wireless network between the UE and BS in Figure 1, packet loss due to the sudden degradation of radio channel quality occurs more often than packet loss due to congestion. TCP can encounter various performance problems in these wireless networks, and many studies have been conducted on the effect of handover and the distance between UEs and base stations on TCP performance in LTE, which is the 4 th generation cellular network [22; 23].  
The mmWave band generally uses a frequency higher than 24 GHz, unlike LTE, which uses the 1.8 GHz band. Thus, the mmWave band, which has a much larger available bandwidth, offers higher throughput than LTE. However, high frequency radio links, where signal attenuation frequently occurs depending on the surrounding environment, present various propagation conditions from the viewpoint of end-to-end connection.  
In line-of-sight (LoS) transmission, the maximum bandwidth of a mmWave signal is greatly reduced by obstacles and changed to non-line-of-sight (NLoS) transmission. A mmWave channel with a high maximum bandwidth may have its signal-to-interference-plus-noise ratio (SINR) reduced by 30 dB or more due to signal blocking, which may significantly reduce the available transmission capacity of the link [14]. In addition, the mmWave signal, which is a high frequency signal, consists of a relatively narrow range of cell networks and exhibits more frequent variability than LTE because signal blocking may be caused by small obstacles, even the human body. Therefore, TCP, which must ensure reliable data transfer between end hosts, may experience severe degradation in the mmWave band due to the high maximum bandwidth and frequent channel fluctuations.