esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Instrumental Analytical Techniques
### Gas Chromatography (GC)
#### 3.1.1 Gas Chromatography Coupled to Flame Ionization Detectors (GC-FID)  
The FID is the most recognized detector for GC because it is very sensitive to volatile compounds with hydrocarbon-based structures and has a mass flux-dependent response, which in turn makes it very feasible for VOC analysis [93]. This detector is very popular for quantitative purposes, after the building of calibration curves (i.e., peak areas versus concentration plots) for each compound, but is not sufficient for accurate compound identification by itself, which is overcome by the use of respective standards (co-chromatography), using linear retention indices in comparison to the information in databases, or working with well-characterized profile samples. Therefore, the analyses of compounds biologically active on insects that employ GC-FID include an additional detector to complement the VOC profiling of a sample, such as the mass spectrometry detector (MSD) [94].  
The determination of absolute amounts of semiochemicals using GC-FID after sampling is possible if the analytical procedure is entirely standardized (sampling and chromatographic analysis) with appropriate quantitative standards for reliable comparisons and validated results [95]. Additionally, some analytical precautions must be taken into consideration if SPME is used for collection to ensure a correct quantitative analysis, since the SPME recovery rate of any individual compound is different as well as volatiles can compete against each other [96]. In this regard, a mixture of the respective standards should be prepared in such relative proportions similar to those presented in the target samples, to be included into internal and external calibration procedures. Only in this way, the GC peak areas can be used to calculate absolute quantities of semiochemicals from SPME samplings [97]. On the other hand, a comparison of relative quantities (using GC relative abundance data) of a particular volatile across samples/treatments/species after SPME (if collected and analyzed under identical conditions) can be performed, but such a comparison of different volatiles within a chromatographic profile is not possible owing to the above-mentioned reasons related to the SPME itself.  
Some examples of recent investigations within the bulk of studies using GC-FID are the identification of sex pheromones of pest insects [46]; specific parasitoid-attracting molecules [37]; volatiles released by plants in the presence of a pollinator [98]; the assessment of alteration in pest response to plants infected with phytopathogens [67]; assessment of essential oils [99], some of which performed fumigant and repellent activity [100], as well as in those studies involving plant VOC profiling [75; 101]. Although plant VOCs are not usually profiled in the context of plant-insect interaction, provides valuable data for further investigation regarding this topic.
## 5 Discussion
### Limitations and Future Directions  
While the OSWS heralds a transformative shift in weather monitoring, certain limitations prevail. These encompass the system's precision, which, while consistent, might be inherently limited due to its cost-effective approach. Another pivotal concern is data transmission and security, especially in regions with unreliable internet connectivity.  
Future endeavors related to the OSWS ought to emphasize:  
1. Secure and dependable data transmission, particularly in remote settings.
2. ExplorING alternative data transmission channels and offline data logging/retrieval solutions.
3. Strengthening data security measures to preserve data integrity during transmission and storage.
4. Investigating durable sensors and materials to withstand environmental challenges, reinforcing the OSWS's reliability.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multirow{2}{*}{\begin{tabular}{c} **Temperature** \\ **RMSE** [°Cl] \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Humidity** \\ **RMSE** [°RH] \\ \end{tabular} } & \multicolumn{2}{c}{\begin{tabular}{c} **Atmospheric** \\ **Pressure** [**ng/m3**] \\ \end{tabular} } & \multicolumn{2}{c}{\begin{tabular}{c} **PM 1 RMSE** \\ **[ng/m3**] \\ \end{tabular} } & \multicolumn{2}{c}{\
\begin{tabular}{c} **PM 25 RMSE** \\ **[ng/m3**] \\ \end{tabular} } & \multicolumn{2}{c}{
\begin{tabular}{c} **PM 10 RMSE** \\ **[ng/m3**] \\ \end{tabular} } \\ \cline{1-1}  & & & & & & \\ \hline This OSWS & 1.62 & 4 & 1 & 2.45 & 3.5 & 4.6 \\ \hline
[20] & 0.99 & 4.16 & 0.53 & * & * & * \\ \hline
[29] & 1.5 & 1.3 & * & * & 11 & * \\ \hline
[21] & 1.37 & * & * & * & * & * \\ \hline
[22; 28] & * & * & * & * & * & * \\ \hline \hline \end{tabular}  
* Indicates that the variable was not compared with a reference.  
\end{table}
Table 8: Comparative table between this OSWS and other projects in RMSE.
# Improved Short-Term Clock Prediction Method for Real-Time Positioning
## 2 Clock Prediction Methods
### New Model for Short-Term Prediction  
If periodic fluctuations in satellite clock sequences are considered, then periodic corrections should be added to the model. In real-time clock prediction, we assume that the clock has similar stability and variations in a short period. The long-term residual sequence of linear prediction is modeled to solve the problem that the few-hour available clock correction is insufficient to obtain low-frequency variations. The coefficients of the periodic term are obtained by fitting the previous long-term prediction results using the results of FFT analysis. The coefficients are then applied to the subsequent forecasting process to improve forecast precision. Equation (3) can be changed to the following formula.  
\[\mathrm{x}(t)=\mathrm{a}_{0}+\mathrm{a}_{1}t+\sum_{i=1}^{\mathrm{N}}b_{t,i} \sin\left(\frac{2\pi}{T_{i}}t-t_{0}\right)+\ \sum_{i=1}^{\mathrm{N}}c_{t,i}\cos\left(\frac{2\pi}{T_{i}}t-t_{0}\right)+ \varepsilon(t), \tag{4}\]  
where \(b_{t,i}\) and \(c_{t,i}\) (\(i=1,2,\ldots,\mathrm{N}\)) are coefficients of the periodic term corresponding to the extrapolated time \(t\), which can be obtained by fitting the residual sequence of the previous period with least squares. \(t_{0}\) is the initial time of the long-term residual sequence. \(T_{i}\) is the corresponding period, and \(\mathrm{N}\) is the number of periods. Sliding forecast is used in the practical application.  
The user can set the extrapolation time according to the demand to densify the prediction sequence. The advantage of this new strategy is that it is possible to estimate a set of periodic term coefficients (\(b_{t,i}\) and \(c_{t,i}\)) for each extrapolated time interval with higher accuracy. The disadvantage is that the longer the predicted length, the greater the computational complexity. It is more suitable for maintaining short-term prediction of the real-time clock.
## 2 Materials and Methods
### Plasma Post-Processing  
For plasma post-processing, a plasma source as described in more detail in previous work [38; 39; 40] was used. This plasma source basically consists of a conical rotation-symmetric high-voltage copper electrode embedded in a plastic housing made of polyoxymethylene (POM). The gas channel for feeding the plasma process gas is found between the high-voltage electrode and the housing. In the present work, argon (Ar 5.0 from Linde Gas AG, Munich, Germany) was used as process gas where the gas flow rate was 4 standard litres per minute. The plasma was ignited between the high-voltage electrode and an external plane ground electrode, both made of copper. The glass samples were placed between the high-voltage electrode and the ground electrode; the working distance between the outlet nozzle of the plasma source and the work piece surface was approx. 10 mm. This setup leads to the formation of a direct dielectric barrier discharge (DBD) plasma where the sample itself is an essential part of the plasma ignition which consequently occurs directly on the sample surface as visualised in Figure 3.  
The plasma source was driven by a pulsed power supply where the voltage of each pulse was \(U=11\) kV, the nominal pulse repetition rate was \(f=7\) kHz and the pulse duration of each pulse was \(\tau=80\) us. Due to the functional principle--the DBD--the plasma gas temperature and sample surface heating, respectively, are quite moderate and amount to approximately 90 \({}^{\circ}\)C as determined via infrared camera measurements in previous work [41]. This is due to the fact that extremely low currents in the range of some microamperes to milliamperes occur during DBD plasma operation. In contrast to classical arc discharges, burn-off of the electrode material is moreover reduced or even inhibited, avoiding the deposition of electrode material on the sample surface. In the present work, the plasma treatment duration was 60 s. The impact of such short-term direct DBD plasma post-processing was  
Figure 3: Experimental setup for plasma post-processing of polished glass samples applying a direct dielectric barrier discharge (DBD) plasma.  
investigated since it was observed in previous work that applying this type of plasma for merely one minute allows considerable smoothing of optical media [39; 40] as well as surface cleaning and an accompanying increase in LIDT of sapphire substrates [16].
## 2 GTSP Solution for UAV Material Drop Paths  
### GTSP Model Construction and Description  
This paper converted the actual material path-planning problem into a GTSP that can be solved computationally, aiming to optimize the material delivery path to minimize the total distance. Emergency airdrop stations are considered critical points for material delivery, and the two-dimensional coordinates of each point represent the actual location of a specific airdrop station. This paper addressed the GTSP by grouping these airdrop stations, each representing a collection of airdrop stations for a particular region. This approach can be utilized to optimize the cruising route of UAVs, ensuring rapid access to all critical groups in emergencies for effective material airdropping. This abstraction allows for the application of efficient algorithms to find the shortest airdrop path that covers all groups. The GTSP problem is represented by defining a weighted graph \(G=(V,E,W)\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Abbreviation** & **Full Form** \\ \hline UAV & Unmanned Aerial Vehicle \\ GTSP & Generalized Traveling Salesman Problem \\ WOA & Whale Optimization Algorithm \\ IWOA & Improved Whale Optimization Algorithm \\ ABC & Artificial Bee Colony \\ WSHA & Whale-Swarm Hybrid Algorithm \\ APCR-YOLOv8 & Attention-based and Parameter and Computational Reduction \\ SimAM & Simple Attention Module \\ GhoHGNetv2 & Ghost HGNetv2 \\ LSCDHead & Lightweight Shared Convolutional Detection Head \\ \hline \hline \end{tabular}
\end{table}
Table 1: Nomenclature table.  
is a set of vertices, and each vertex \(v_{i}\) represents an emergency airdrop station. \(E\) is a set of edges, representing possible connections between drop stations, and the edge \(e_{ij}\) represents a path from the drop station \(v_{i}\) to \(v_{j}\). \(w\) is a set of weights, representing the weight of a path from \(v_{i}\) to \(v_{j}\) and \(w_{ii}=0\) for all \(i\). In the GTSP of this paper, the drop stations were divided into a total of m groups. The goal is to find the shortest closed path that contains precisely one airdrop station in each group, which ensures that each group is dropped at least once while the total material drop path is as short as possible. The path can be described by a sequence of vertices \(\left(v_{p_{1}},v_{p_{2}},\ldots,v_{p_{m}}\right)\), where \(v_{p_{i}}\in V_{i}\), each \(V_{i}\) is a collection of material airdrop stations.  
This paper aimed to optimize path selection by minimizing the overall weight of the pathways as represented by the following equation:  
\[\text{MinD}=\sum_{i=1}^{m-1}w_{v_{p_{i}}v_{p_{i+1}}}+w_{v_{pn}v_{p_{1}}} \tag{1}\]  
where \(m\) is the total number of vertices in the vertex set \(V\) and \(v_{p_{i}}\) is the index of the \(i\)th vertex in the path.
## 4 Discussion  
In the practical application of along-track multichannel SAR, the channel error between channels mainly arises from the SAR system error, the baseline error, and the squint angle error. Related research illustrates that the channel error can influence the performance of channel cancellation operations. Specifically, multichannel SAR working at a higher frequency could be more sensitive to channel errors. In this situation, channel errors should be compensated for precisely before the multichannel SAR channel cancellation operation is performed.  
This paper mainly focuses on the research on dual-channel SAR systems. As for N-channel SAR systems, the problem of dark straps can be perfectly solved after channel cancellation operations. In the N-channel SAR system, one channel can be chosen as the counter reference. Therefore, the channel cancellation operation of the N-channel SAR system can be regarded as a concatenation of multiple dual-channel SAR systems in which the proposed estimation method of IPSR based on cosine similarity can still be applied effectively.  
In our future work, we will focus on the combination of channel error compensation and DCC operation for dual-channel SAR system. Moreover, the channel cancellation of the N-channel SAR system with much lower JSR will also represent the main direction of our future research.
# An Energy-Efficient Mobile Sink-Based Unequal Clustering Mechanism for WSNs
## 2 Related Works
This scheme could enhance the network lifetime and achieve a high packet delivery ratio by using multiple mobile sinks with a fixed speed. However, the reduced sink velocity increased the data delivery latency [40], whereas, using both types of sink can solve the data delivery latency problem and also increases the packet delivery ratio. This paper attempts to solve the energy hole problem by engaging the MSs with unequal velocities. In addition, it is considered that the velocities of MSs are separately adjusted in different angles.
## 4 Algorithms and Experimental Results
### FastTree
By employing optimal data structures, which are used for disjoint-set unions [18], the amortized time complexity of Steps 11 and 12 will be \(O(\alpha(n))\), where \(\alpha(n)\) is the inverse of the function, \(f(x)=A(n,n)\), and \(A\) is the well-known fast-growing _Ackermann_ function. Furthermore, Step 16 takes \(O(n\alpha(n))\) time. Hence, the running time of FastTree would be \(O(m+\alpha(n)n^{2})\). Since \(A(4,4)=2^{2^{2^{65536}}}\), \(\alpha(n)\) is less than four for any practical input size, \(n\). In comparison to the fast version of [PERSON] _et al._'s algorithm, FastTree employs a simpler data structure and, in comparison to [PERSON] _et al._'s original algorithm, it has lower time complexity. Yet, the most important advantage of FastTreeto [PERSON] _et al._'s algorithm is that it will not stick if there is not a consistent tree with the input triplets, and it will output a proper tree in such a way that the clusters are very similar to that of the real network. The tree in Figure 2 is the output of FastTree on a dense set of triplets based on the yeast, _Cryptococcus gatti_, data. There is no consistent tree with the whole triplet set; however, [PERSON] _et al._[19] presented a level-2 network consistent with the set (see Figure 3). This set is available online [20]. In comparison to BPMR and BPMF, FastTree runs much faster for large sets of triplets and species. However, for highly sparse triplet sets, the output of FastTree may satisfy considerably less triplets than the tree constructed by BPMF or BPMR.
# Delay-Sensitive NOMA-HARQ for Short Packet Communications
###### Abstract
The delay performance is optimized for single user with HARQ in the finite blocklength regime in the Rayleigh fading channel in [29]. HARQ was recently analyzed with NOMA in the downlink set up with two users in [30; 31; 32; 33], where the outage performance was analyzed in the infinite blocklength regime with rate and power adaptation. HARQ-enabled NOMA is also studied in [22; 23] to evaluate its usefulness in enabling URLLC and mMTC. In [34], the authors analyzed HARQ in an uplink NOMA setting, focusing on enabling retransmissions to be distinguishable from the regular transmission to facilitate grant-free HARQ communication. Moreover, in [35], the authors adjust power levels among users to reduce retransmission requests. Retransmission with HARQ causes additional delays in communication. Efforts have been made to improve retransmission quality resulting in throughput gain [36]. However, the throughput gain only translates to average delay performance improvements [37]. URLLC and many mMTC scenarios require low-latency with a per-packet delay guarantee. In this paper, we consider an uplink NOMA system paired with HARQ for short packet communications, where the target per-packet latency of each user is the priority. Although the HARQ process improves reliability, it increases latency and causes packet queuing. The primary motivation of this work is to increase reliability without causing latency by maintaining per-packet arrival deadlines. We propose a novel NOMA-HARQ approach, where the retransmission of each packet is served non-orthogonally with the new packet in the same time slot. We use a Markov model (MM) to analyze the dynamic of the uplink NOMA-HARQ with one retransmission and characterize the PER, throughput, and latency performance of each user. We also present numerical optimizations to minimize PER and find the optimal power ratios of each user. Numerical results show that the proposed scheme significantly outperforms the standard NOMA-HARQ in terms of the packet delivery latency at the target PER. The rest of the paper is organized as follows. In Section 2, the system model and preliminaries on NOMA and HARQ are presented. Section 3 presents the proposed delay-sensitive NOMA-HARQ scheme, where its reliability and delay analysis are discussed. Section 4 presents numerical results. Finally, Section 5 concludes the paper.
## 4 Innovation of the TPM Methodology Regarding the Division of Preventive Maintenance
### Periodic Prevention (PP)--Expiry-Driven Maintenance  
The last block, yet not the least important, of preventive maintenance is the PP block, which is expiry-driven. It concerns replacement of components that must be undertaken based on producer's recommendation. Unlike the AM block, although it concerns a fixed time period, it is different from AM and its execution is not compulsory as it does not concern a legally-regulated expiration. Certain processes are legally-regulated, such as revisions of electrical distribution boards that must be checked annually, which is done within the PP block before the expiration. This period must not be breached and must be in compliance with the law. Other processes are prescribed by the producer that determines the part lifetime after being installed, i.e., there are so-called operating hours saying how many hours the component should be operational. This period can be extended, although with a higher risk of failure occurrence. This block overlaps with the preventive maintenance block of component replacement, the so-called PCR method--planned component replacement. This period does not state the lifetime (Figure 12), which is calculated using MTTR (mean time to recovery, repair, respond, or resolve). This determines the average component lifetime until a failure occurs; in other words, it indicates what is the average life of the component without repairs.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**ID** & **77,749** & **77,744** \\ \hline Machine & B & A \\ \hline Date & 5 July 2020 & 5 July 2020 \\ \hline Period & PM & PM \\ \hline Type of prev & Electromechanic & Mechanic \\ \hline \multirow{2}{*}{Description} & Abnormality ID: 1118; Please, place the cable from & Abnormality ID: 1068; Unknown sand leakage (sand \\  & the hydraulic ramp into a refractory board. 380 V & falling on people’s heads). Check cyclone and suction \\  & and it is burnt through. & piping condition \\ \hline Completed & Yes & Yes \\ \hline OK/NOK & Yes & No \\ \hline Countermeasure & & Temporarily plastered. More extensive repair suitable \\ \hline Repaired & Yes & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Example of generated abnormalities/requests.  
The curve reveals that the most critical period in the component lifecycle is when the component is installed. Then, the curve exponentially falls, and the failure probability is very low. The component is operational until its lifetime comes to an end, which exponentially increases the probability of a failure occurrence.
## 3 Results and Discussion
### Setting Time and Consistency
In the LC\({}^{3}\)-50 systems, the maximal addition of AVM only improved the consistency by achieving a 6.2% change from the reference samples. In the OPC system, AVM was unstable, as increasing the plasticizer dosage led to low consistency. That might be attributed to the low surface area of cement particles and the volume of water available from AVM compared to CS [49]. The result of AVM can be explained by [PERSON] et al. [50], who pointed out that phenols react with calcium hydroxide (Ca(OH)\({}_{2}\)) in cement during the hydration processes. The phenol active part (OH) from AVM adsorbs on the cement ions, causing steric hindrance that helps retain flow and thus improve consistency with increases in percentage dosage.