esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 System Architecture
### Communication Network  
The communication network is a hybrid system that combines Radio Frequency (RF), satellite, and cellular technologies to ensure reliable data transmission across the geographically dispersed infrastructure of the Lerma Basin [8].  
* Radio Frequency: RF is used for short-range communication between RTUs and local hubs, providing a cost-effective solution for areas with limited cellular coverage [7].
* Satellite Communication: Satellite links are used for remote locations where RF and cellular networks are unavailable, ensuring continuous data transmission [10].
* Cellular Network: Cellular networks provide high-speed data transmission for areas with good coverage, enabling real-time monitoring and control [13].
* Network Security: The communication network is protected by robust cybersecurity measures, including encryption and access control, to prevent unauthorized access and data breaches [19]. Since the system works over HTTPS, the secure variant of HTTP, the encryption of data applies a Transport Layer Security (TLS) protocol and a Secure Sockets Layer (SSL). TLS is an encryption system that employs two keys: a public key for anyone connecting with the server and a private key stored for the server itself.
# The Implementation of Precise Point Positioning (PPP): A Comprehensive Review
## 2 Error Sources in PPP
### Site Displacement Effects  
PPP offers an absolute global solution in alignment with global terrestrial frames, such as the International Terrestrial Reference Frame (ITRF). The earth's crust experiences displacements due to various natural phenomena, such as solid Earth tides, polar tides, ocean loading, atmospheric pressure loading, and accumulations of groundwater and snow. While such displacements might be deemed negligible in relative positioning over concise baselines, their implications are significant for an ITRF-compatible PPP solution [63].  
This section addresses primary displacement effects, particularly those exceeding 1 cm, such as solid Earth tides, polar tides, and ocean loading. The secondary effects can be safely ignored for centimeter-level positioning. The vector \(\mathbf{\Delta r}\) denotes the total site displacement in the ECEF frame. This vector must be added to the calculated receiver position vector \(\mathbf{r_{r}}\) to calculate its position in the ITRF \(\mathbf{r_{ITRF}}\) as follows:  
\[\mathbf{r_{ITRF}} =\mathbf{r_{r}}+\mathbf{\Delta r}\] \[=\mathbf{r_{r}}+(\mathbf{\Delta r_{solid}}+\mathbf{\Delta r_{ polar}}+\mathbf{\Delta r_{ocean}}) \tag{22}\]  
The following subsections describe the details of each displacement component in (22).
## 1 Introduction
A thorough analysis that takes intoaccount the power consumption of the microcontroller and the wireless transmitter on the sensor unit and the seizure detection performance is hence crucial when developing data reduction techniques for a wireless seizure detection system. Such analysis has not been considered in previous works. In this paper, we present energy-efficient data reduction approaches for reducing transmission data in a wireless EEG seizure detection system. Specifically, we look at two data reduction approaches, compressive sensing-based EEG compression and low-complexity feature extraction and transmission. The performance is quantified in terms of seizure detection effectiveness and power consumption. The goal is to assess the use of data reduction methods for minimizing the power consumption at the sensor side, while maintaining high seizure detection performance. The tradeoffs between seizure detection performance and power consumption when choosing system parameters are also discussed. The paper is organized as follows: In Section 2, we present the setup of a wireless seizure detection system and describe different data reduction approaches. In Section 3, we describe the evaluation methodologies for assessing seizure detection effectiveness and the system power consumption of each approach. In Sections 4 and 5, we present the analysis results and discuss the tradeoffs. Finally, the conclusion is presented in Section 6.
## 5 Conclusions  
This study investigated the pairwise error probability bounds of cooperative DF MIMO systems using one-bit ADCs over Rayleigh fading channels. The analysis focused on the approximate joint maximum likelihood detection, which was designed for mitigating the error propagation effects commonly observed in DF relaying. A tractable upper bound on the PEP was derived using [PERSON]'s inequality and Chernoff's bound. The asymptotic characteristics of the PEP were analyzed to gain further insight into system behavior, revealing how one-bit quantization affects the achievable diversity gain. A key finding was that approximate joint maximum likelihood detection achieved full diversity only when the symbol pairs were sign-inverted and the relay correctly decoded the source symbol vector. The diversity gain was limited to that provided by the source-to-destination link when the relay failed to decode correctly. This represents the first analytical performance characterization of approximate joint maximum likelihood detection for cooperative DF MIMO systems with one-bit ADCs because the nonlinearity of the sign function in one-bit quantization makes the analysis particularly challenging. The validity of the proposed analytical bounds was confirmed through Monte Carlo simulations, which demonstrated excellent agreement with the theoretical predictions.  
A potential future direction for this study is to extend the analytical framework to scenarios involving multiple sources, relays, and destinations. In such multi-node settings, the relay's transmission protocol may differ significantly from the single-node case, depending on the underlying cooperation strategy. Consequently, the analytical approach must be adapted accordingly, making this a distinct line of research beyond the scope of this study.  
This research received no external funding.  
The data presented in this study are available on request from the corresponding author.  
The author declares no conflicts of interest.
# Impact of Environmental Factors on Stilbene Biosynthesis
## 2 Biosynthesis of Stilbenes and Stilbenoids
### Prepulation  
Prenylated stilbenoids have been isolated from a restricted number of stilbene-producing plants including _Macaranga_ spp. (Euphorbiaceae) [135; 136], _Glycyrrhiza_ spp. [137; 138; 139; 140], peanut [141; 142; 143; 144; 145; 146], and mulberry [147].  
Figure 6: Common methylated stilbenes are biosynthetically derived from pinosylvin, resveratrol, and piceatannol.  
Over 45 prenylated stillbenoids have been identified in _A. hypogaea_[148]. The major prenylated stillbenoids accumulated in peanuts are _trans_-arachidin-1, _trans_-arachidin-2, _trans_-arachidin-3, and _trans_-3'-(3-methyl-2-butenyl)-resveratrol (Figure 7) [148]. In accordance with their role as phytoalaxins, peanut prenylated stillbenoids have been shown to accumulate upon challenge with microorganisms [142; 143; 144; 145; 149] and to possess remarkable antifungal activity [150; 151; 152]. Several recent studies have shown interesting therapeutic potential for peanut prenylated stillbenoids [135; 136; 153; 154]. Prenylated compounds generally exhibit greater bioavailability than their non-prenylated counterparts, due to the increase in lipophilicity linked to the prenyl groups. Despite their biological and medical relevance, the biosynthetic pathways of prenylated stillbenoids have yet to be elucidated and the genes encoding stilbenoid-specific prenyltransferases have only recently been identified in plants.  
In 2018, membrane-bound stilbene-specific prenyltransferases have been described in peanut and mulberry [148; 155]. Combining targeted transcriptomic and metabolomic analyses, [PERSON] et al. [148] discovered five candidate prenyltransferase genes in elicitor-treated _A. hypogaea_ hairy root cultures. Two of these, _AhR4 DT-1_ and _AhR3 DT-1_, were functionally characterized in a transient expression system consisting of _Agrobacterium-infiltrated leaves of _Nicotiana benthumiana_ Domin. The authors demonstrated that AhR4 DT-1 catalyzes the prenylation of resveratrol at its C-4 position leading to arachidonic-2 formation, while AhR3 DT-1 is responsible for resveratrol prenylation at C-3' leading to the 3-methyl-2-butenyl-3-resveratrol formation. In 2018, [PERSON] and colleagues [155] identified and functionally characterized a stilbenoid-specific prenyltransferase from _M. alba_ (MaOGT) that recognizes oxyresveratrol and geranyl diphosphate (GPP) as natural substrates and catalyzes oxyresveratrol prenylation. Both peanut and mulberry prenyltransferases have proved to be highly specific for stilbene substrates and fluorescent microscopy analysis has shown that they are localized in the chloroplast, similarly to other membrane-bound plant prenyltransferases [156].
## 3 Results and Discussion
### Effect of pH  
Figure 4a shows the adsorption performance of MPC toward MB removing at various pH values from 3 to 11. Note that the amount of absorbed MB by MPC adsorbent gradually increases with the elevation of solution pH. The results indicate the significant role of solution pH on the performance of MPC adsorbent. It was reported that surface property of adsorbent and structure of dye molecules played important roles in determining the relationship between the adsorption capacity and pH values [1]. Previous results have indicated that the adsorbent with the presence of oxygen-containing groups is negatively charged in basic solution [26]. Figure 4a shows the Zeta potentials of MPC in different solution pH. A decreased Zeta potential is observed with the investigated pH range and the negatively charged surface of the MPC was observed in the pH range from 4-10. Note that the number of negatively charged surface sites on MPC increases with the elevation of the solution pH from 4 to 10 [1]. Therefore, the positive effect of solution pH on the adsorption capacity of the MPC must be ascribed to the increase of electrostatic attraction between the cationic MB molecule and the negatively charged surface of MPC. The optimal performance of MPC for MB adsorption was achieved at pH = 11 with the highest adsorption capacity of 214.4 mg g\({}^{-1}\). Of special note, the MPC presents the best  
Figure 3: N\({}_{2}\) adsorption-desorption isotherms of **(a)** PC and **(b)** MPC; **(c,d)** Plots of q\({}_{e}\) vs. contact time toward MB adsorption on MPC at different MB concentrations (C\({}_{0}\): 45, 50, and 55 mg L\({}^{-1}\), 298 K, pH = 11) and the corresponding removal efficiency.  
adsorption capacity of MB compared to the reported adsorbents, such as Fe\({}_{3}\)O\({}_{4}\)/CNPs [27], MCS [28], magnetic graphene-carbon [26], MPCMs-0.25 [29], \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)/C NP [30], Fe\({}_{3}\)O\({}_{4}\)@ZIF-8 core-shell [31], hydrotalcite-dodecylsulfate/Fe oxide [32], magnetic cellulose/GO [33], magnetic ZnO/ZnFe\({}_{2}\)O\({}_{4}\)[34], and RGO/Fe\({}_{3}\)O\({}_{4}\)[35] (Figure 4b and Table S1).
## 1 Introduction
The controllers use that information to tune the forwarding algorithm parameters through configuration policies emitted to the nodes to obtain a better network performance. * Use of the controller-driven OppNet architecture for tackling the congestion in an OppNet characterized by a high unpredictability of the nodes' mobility and a multi-copy replication forwarding strategy. Specifically, the OppNet's controllers orchestrate the value of the replication limit of the forwarding algorithm based on the buffer occupancy readings gathered by the nodes. * An evaluation of the performance and benefits of the controller-driven OppNet for the use case of congestion control. To evaluate our proposal, we have simulated diverse network scenarios based on real and synthetic mobility traces using different message generation distributions. We have evaluated the controller-driven OppNet on the basis of the standard performance metrics for OppNets. Furthermore, we have run the aforementioned simulations over an OppNet without the control layer (context oblivious) using an epidemic and a quota-based forwarding protocol. We have compared and evaluated the performance of both configurations. We have proven that a controller-driven OppNet performs better than a context-oblivious one. The rest of the paper is structured as follows. Section 2 introduces the related work in the field of opportunistic networks, focusing on data forwarding and congestion control. Section 3 presents the controller-driven OppNet architecture. Next, in Section 4, the controller-driven OppNet architecture is used to manage congestion. The paper follows with Section 5, where through simulation-based experimentation, we evaluate the performance of the controller-driven OppNet congestion use case, and we compare these results with the performance obtained by a non-controlled OppNet. Finally, Section 6 contains the conclusions drawn from this work.
## 4 Experiments and Results
### Experimental Methodology
#### 4.1.2 Object Detection  
Object detection and surveillance have become common applications associated with the widespread deployment of surveillance cameras. Autonomous video surveillance is the process of analyzing video sequences using object detection, segmentation, and classification for various applications. As object detection from multiple surveillance cameras is one of the use cases of stream processing, we deploy an object detection application on the Apache Flink stream processing platform. The application is adopted from [66], and it is a traffic detection application that employs the YOLO V2 model for the detection of images from a traffic flow. In addition to the YOLO V2 model, we also evaluate the Tiny YOLO model to better understand both of the models' performance on resource-constrained devices. YOLO V2 is used for the detection of larger numbers of object categories, while Tiny YOLO is designed for limited categories of objects with faster detection. We investigated the inference workflow and performance of YOLO-based object detection for edge, fog, and cloud layers. A traffic video that consisting of 5792 sequence frames was used as the test dataset for object detection. We used the prediction time per frame and detection rate performance metrics to evaluate the Flink streaming application's performance in each computing paradigm. Prediction time per frame is an important metric when dealing with real-time streaming applications like video analytics or object detection, where low latency is paramount [67]. Prediction time per frame provides insights into the responsiveness of the Flink platform's stream processing application, ensuring the timely processing of incoming data. The detection rate is a metric used to assessing the accuracy and effectiveness of the Flink streaming application in identifying relevant patterns or objects within the streaming data [68].
# External Slot Relationship Memory for Multi-Domain Dialogue State Tracking
## 5 Experimental Results  
### Main Results  
We present the main results of our approach in Table 3, where ER-DST is compared with several other state-of-the-art methods. The evaluation of our model's performance is conducted using joint goal accuracy, which is consistent with the evaluation approach used for these methods. ER-DST specifically refers to our proposed model equipped with the external slot relation memory.  
Table 3 clearly demonstrates the significant performance improvement achieved by ER-DST on both datasets. Specifically, on the MultiWOZ 2.0, our method achieves a joint goal accuracy of 54.76%. Additionally, on the improved MultiWOZ 2.1, we achieve a joint goal accuracy of 56.75%.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **Description** \\ \hline CSFN-DST & A pattern graph with context encoding \\ SOM-DST & a fixed-sized memory and selectively overwritten \\ TripPY & three copy mechanisms \\ STAR & a self-attention network \\ \hline \hline \end{tabular}
\end{table}
Table 2: Introduction of advanced methods.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **MultiWOZ 2.0** & **MultiWOZ 2.1** \\ \hline CSFN-DST & 51.57 & 52.88 \\ SOM-DST & 51.72 & 53.01 \\ TripPy & - & 55.29 \\ STAR & 54.53 & 56.36 \\ \hline ER-DST (ours) & 54.76 & 56.75 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The joint goal accuracy (%) tested on the MultiWOZ 2.0 and MultiWOZ 2.1.  
Table 4 presents the experimental results of the ER-DST and the baseline models on the accuracy of specific domain slots in the MultiWOZ2.1. The results indicate that ER-DST outperforms the STAR in the specific domains of attraction, hotel, and taxi. Notably, ER-DST achieves significant JGA in the domains of hotel and taxi, where the data is relatively limited, with accuracies of 53.21% and 66.85%, respectively. This is attributed to ER-DST's ability to learn the most stable relationships between slots from the entire dataset, resulting in performance optimization for domains with initially fewer training samples.
# Research Progress of Dynamic Measurement Technology of Atom Gravimeter
## 3 Main Error Sources and Suppression Methods of Dynamic Measurement  
The atom gravimeter has excellent performance in laboratories where environmental factors are well controlled, and its sensitivity and accuracy can exceed those of a classical gravimeter, but its volume is usually large. The atom gravimeter hat can be used for field measurement and requires high stability and reliability, as well as small size, easy movement, low power consumption, and so on. Under dynamic conditions, the atom gravimeter not only needs to have the above characteristics but also needs to overcome the additional vibration noise introduced in the dynamic environment and the influence of system effects. The main error sources and suppression methods of atom gravimeter dynamic measurement are listed below.
# Parameterised Enumeration for Modification Problems +
## 4 Generalisation to Modification Problems
### Weighted Satisfiability Problems  
Finally, we consider satisfiability questions for formulas in the Schaefer framework [8]. A constraint language \(\Gamma\) is a finite set of relations. A \(\Gamma\) formula \(\phi\), is a conjunction of constraints using only relations from \(\Gamma\) and, consequently, is a quantifier-free first order formula.  
As opposed to the approach of [PERSON] et al. [13], who examined maximum satisfiability, we now focus on the problem MinOnes-SAT\((\Gamma)\) defined below.  
If \(\theta\colon X\to\{0,1\},\theta^{\prime}\colon Y\to\{0,1\}\) are two (partial) assignments over some set of variables \(X\) and \(Y\), then \(\theta\subset\theta^{\prime}\) is true, if \(\theta(x)=\theta^{\prime}(x)\) for all \(x\in X\) and \(X\subset Y\).  
Definition 18 (Minimality): Given a propositional formula \(\phi\) and an assignment \(\theta\) over the variables in \(\phi\) with \(\theta\models\phi\), we say that \(\theta\) is minimal if there does not exist an assignment \(\theta^{\prime}\subset\theta\) and \(\theta^{\prime}\models\phi\). The size \(|\theta|\) of \(\theta\) is the number of variables it sets to true.  
Formally, the problem of interest is defined with respect to any fixed constraint language \(\Gamma\):  
\begin{tabular}{l l} \hline \hline
**Problem:** & Min-MinOnes-SAT\({}^{\textsc{size}}(\Gamma)\) \\ \hline
**Input:** & \((\phi,k)\), a \(\Gamma\) formula \(\phi,k\in\mathbb{N}\). \\
**Parameter:** & The integer \(k\). \\
**Output:** & Generate all inclusion-minimal satisfying assignments \(\theta\) of \(\phi\) with \(|\theta|\leq k\) by \\  & non-decreasing size. \\ \hline \hline \end{tabular}  
Similarly, the problem Enum-MinOnes-SAT\((\Gamma)\) asks for all satisfying assignments \(\theta\) of \(\phi\) with \(|\theta|\leq k\). In this context, the operations in \(\Omega\) are functions that replace the variable with index \(i\in\mathbb{N}\) by true.  
Theorem 6: _For all constraint languages \(\Gamma\), we have: Min-MinOnes-SAT\({}^{\textsc{size}}(\Gamma)\) is FPT enumerable and Enum-MinOnes-SAT\({}^{\textsc{size}}(\Gamma)\in\mathrm{DelayFPT}\) with exponential space._  
Proof: For the first claim we can simply compute the minimal assignments by a straightforward branching algorithm: Initially, begin with the all 0-assignment, then consider all unsatisfied clauses in turn and flip one of the occurring variables to true. The second claim follows by a direct application of Corollary 7.
# Use of SMAP Soil Moisture and Fitting Methods in Improving GPM Estimation in Near Real Time
## 2 Study Area and Data Sets  
Mainland China is selected as the study area. It spans a variety of climate zones: subtropical in the Southern China, temperate monsoon in Northeastern and Central China, temperate continental in Northwestern and Northern China, and plateau in Western China. The nation-averaged annual precipitation over China is approximately 630 mm, with a decreasing trend from the southeast coast to the northwest inland.
## 3 Review of Previous Works
### Throughput Estimation Model  
The throughput estimation model is used to estimate the throughput of a wireless communication link between a source node and a destination node in a WLAN. First, it estimates the _received signal strength_ (_RSS_) using the log-distance path-loss model at the destination node [49]. Then, the sigmoid function is used to convert the RSS into throughput.
# Harmonic Detection Method Based on Parameter Optimization VMD-IWT Combined Noise Reduction
## 5 Test Signal Verification  
The method proposed in this paper is validated using actual arc furnace signals [27]. The sampling method remains consistent with the previously described approach, and the parameter details are outlined in Equation (17). This signal comprises a 50 Hz fundamental wave, 125 Hz, and 25 Hz interharmonics, along with random Gaussian white noise interference, resulting in a signal-to-noise ratio of approximately 26 dB.  
\[f^{\prime}(t)=100 cos(100\pi t+20)+74.813 cos(250\pi t+9)+64.933 cos(50\pi t+93)+ noise \tag{17}\]
## 3 Results and Discussion
### Electrical Conductivity (EC)  
The EC of polymers hinges significantly on the mobility and quantity of charge carriers, closely linked to the material's morphology and chemical composition. Factors such as type, crystallinity degree, and tactile properties are pivotal in evaluating polymers' electrical traits [33; 34]. EC was determined for all products that underwent the previously described experimental conditions; the results are displayed in Table 2. As shown in Table 2 the polymer solids exhibited notably high conductivity at standard room temperature. Specifically, the POA sample displayed a conductivity of approximately 0.34 S-cm\({}^{-1}\), whereas the PPA sample showed a lower conductivity of 0.22 S-cm\({}^{-1}\). This discrepancy confirms that the PPA sample holds the least concentration of emeraldine salts, whereas the POA boasts the highest concentration. However, the introduction of nanocomposites resulted in a considerable decrease in conductivity levels. This reduction primarily stems from stereochemical variations among these nanocomposites.  
The oxidized polymer displayed an almost linear structure, contributing to a minimal ionization potential because of the robust delocalization of electrons. This structural characteristic significantly impacts its conductivity. In this context, the decline in conductivity of nanocomposites suggests that the presence of TiO\({}_{2}\) nanoparticles hampers or disrupts electron transportation pathways within the polymers. This interference likely occurs by potentially diminishing the length of polymer chains, which affects the material's overall conductivity.