esa-sceva/satcom-chunk-collection huggingface dataset:
# Cooperative Communication Based Protocols for Underwater Wireless Sensors Networks: A Review
## 5 Numerical Results
### Link Layer Cooperative ARQ Performance
It is worth noting that the achieved throughput is in the order of hundreds of bits per second, as we consider a transmission rate equal to 1 kbps based on BPSK modulation. By increasing the data rate, for instance through the adoption of a more spectrally efficient modulation, throughput would scale accordingly. Figure 8: Throughput performance of cooperative SW-HARQ. Figure 7: Throughput performance of cooperative SW-ARQ.
## 5 Conclusions  
In this study, foam and lightweight aggregates were used to achieve improved constructability and lightweight of light transparent concrete, and experiments were conducted to improve the workability to the extent that concrete can be poured tightly into the mold. As a result of the experiments, the optimum mix proportions that can reduce the unit weight of the materials and can ensure excellent flowability without material separation were derived, and the conclusions shown below were obtained based on the study results.  
1. The mix conditions for the realization of lightweight and for workability improvement include 45% W/C, 52.5% synthetic foaming agent, 17.5% ALA lightweight aggregate, 25% fly ash, and 0.2% PVA, and are expected to make it possible to manufacture light transparent concrete with good flowability and without material separation.
2. The unit weight of the specimen fabricated with the above mix proportion was 564 kg/m\({}^{3}\), showing an approximate 80% weight reduction compared to that of ordinary concrete (2383 kg/m\({}^{3}\)), which suggests that the work efficiency can be improved in the placement of concrete specimens and field applications.
3. The mixing of PVA fibers was found to be effective in improving the consistency by preventing the aggregate from floating due to the addition of lightweight aggregate with a low specific gravity.
4. The results of the load transfer factor (LTF) analysis showed that the load transfer capacity from the concrete matrix to the fiber was highest in the specimen without plastic bars, followed by the specimens with 5 and 10 mm plastic bars.
5. The total toughness of light transparent concrete was increased with the addition of PVA fibers, and the insertion of bars resulted in a reduction of the concrete matrix area, thus reducing the efficiency of the load transfer between the concrete matrix and the fiber.
6. The additional improvement of compressive and flexural strengths by adjusting mixing proportions or material compositions in the further research is required to apply non-structural parts of buildings, even though it showed the enhanced possibility of workability and post-cracking behaviors.  
This research was supported by a Grant (NRF-2016R1C1B2009489) from the National Research Foundation of Korea (NRF).  
[PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
**ALA** & Autoclaved Lightweight Concrete Aggregate \\
**SFA** & Synthetic Foaming Agent \\
**AFA** & Animality Foaming Agent \\
**FA** & Fine Aggregate \\
**CA** & Coarse Aggregate \\
**LVDT** & Linear Variable Differential Transformer \\
**KS** & Korea Standard \\
**PVA** & Polyvinyl Alcohol \\
**LTF** & Load Transfer Factor \\ \end{tabular}
# SSKM_DP: Differential Privacy Data Publishing Method via SFLA-Kohonen Network
## 6 Experimental Evaluation
### Experimental Evaluation Indexes  
This experiment used mean square error (MSE) and record linkages (RL) to evaluate the performance of the SSKM_DP algorithm. In the SSKM_DP algorithm, the data utility is measured by the information loss caused by the noise added to the original data to satisfy differential privacy. Information loss is generally quantified by the mean square error.  
The mean square error (\(MSE\)) is defined as the mean sum of the squares of the attribute distance errors between the published data _Ue_, satisfying differential privacy and the original data \(U\). The calculation method is shown in Equation (22):  
\[MSE=\frac{\sum\limits_{u_{j}}\sum\limits_{j_{\ell}\in u_{j}}\left[d_{j}(a_{j}^ {i},(a_{j}^{i})_{\ell})\right]^{2}}{n} \tag{22}\]  
In the formula, \(d_{j}()\) represents the Euclidean distance defined by Equation (3); \(u_{j}\) is an attribute of dataset \(U\), and the \(a_{j}^{i}\) and \((a_{j}^{i})_{e}\) distribution represents the \(i\)th attribute value of the \(j\)th record and its corresponding record to be published. The larger the MSE, the more serious the information loss and the lower the availability of published data.  
In the SSKM_DP algorithm, the privacy protection ability is measured by information disclosure. Disclosure is defined as the percentage of the original record that correctly matches the record in the published dataset. Information disclosure is usually represented by the recorded association. The smaller the \(RL\), the lower the degree of information disclosure and the higher the ability of privacy protection. The calculation method is shown in Equation (23):  
\[RL=\frac{\sum\limits_{u\in L}P_{r}(u_{e})}{n}\times 100\,\% \tag{23}\]  
In the formula, \(P_{r}(u_{e})\) represents the probability of association of published record \(u_{e}\), and the formula is as follows:  
\[P_{r}(u_{e})=\left\{\begin{array}{c}\frac{1}{|U_{e}|},u\in U_{e}\\ 0,u\
otin U_{e}\end{array}\right. \tag{24}\]
# A Novel Wavelet Selection Method for Seismic Signal Intelligent Processing
###### Abstract  
Wavelet transform is a widespread and effective method in seismic waveform analysis and processing. Choosing a suitable wavelet has also aroused many scholars' research interest and produced many effective strategies. However, with the convenience of seismic data acquisition, the existing wavelet selection methods are unsuitable for the big dataset. Therefore, we proposed a novel wavelet selection method considering the big dataset for seismic signal intelligent processing. The relevance \(r\) is calculated using the seismic waveform's correlation coefficient and variance contribution rate. Then values of \(r\) are calculated from all seismic signals in the dataset to form a set. Furthermore, with a mean value \(\mu\) and variance value \(\sigma^{2}\) of that set, we define the decomposition stability \(w\) as \(\mu/\sigma^{2}\). Then, the wavelet that maximizes \(w\) for this dataset is considered to be the optimal wavelet. We applied this method in automatic mining-induced seismic signal classification and automatic seismic P arrival picking. In classification experiments, the mean accuracy is 93.13% using the selected wavelet, 2.22% more accurate than other wavelets generated. Additionally, in the picking experiments, the mean picking error is 0.59 s using the selected wavelet, but is 0.71 s using others. Moreover, the wavelet packet decomposition level does not affect the selection of wavelets. These results indicate that our method can really enhance the intelligent processing of seismic signals.  
seismic signal; wavelet transform; wavelet selection; CNN; RNN Article
## 4 Experiments
### Ablation and Sensitivity Analysis  
An ablation study was carried out on the CL-SR framework to determine the individual impact of its components on the overall effectiveness. This involved systematically removing certain components, adjusting key parameters, experimenting with different contrastive learning frameworks, and testing various oversampling algorithms.
## 3 SOC Balancing
### SOC Balancing Control with Different Capacitance  
As the cycle count accumulates, supercapacitor capacitance inevitably differs due to aging effects. To maintain optimal system performance under these conditions, the power allocation strategy must consider remaining capacity as a critical factor, adjusting power in proportion to each supercapacitor's remaining capacity.  
The total capacitance of supercapacitors in the arm \(C_{\text{sc\_,}\_j}\), the total capacitance of the phase \(C_{\text{sc\_,}\_j}\), and the total system capacitance across all three phases \(C_{\text{sc\_,}\_tot}\) are defined as:  
\[\left\{\begin{array}{l}C_{\text{sc\_,}\_j}=\sum\limits_{i=1}^{N}C_{\text{sc\_,}\_ji}\\ C_{\text{sc\_,}\_j}=\sum\limits_{\tau}^{\text{p}\text{n}}C_{\text{sc\_,}\_rj} \\ C_{\text{sc\_,}\_tot}=\sum\limits_{j}^{\text{a}\text{b},\text{C}}C_{\text{sc\_,}\_j}\end{array}\right.. \tag{24}\]  
The reference power for supercapacitors is determined through remaining capacity at both the phase and arm, expressed as:  
\[P_{\text{sc\_,}\_rj}^{\text{ref}}=\frac{C_{\text{sc\_,}\_rj}}{C_{\text{sc\_,} \_tot}}P_{\text{sc}}^{\text{ref}}. \tag{25}\]  
Under conditions of non-uniform supercapacitor capacitance distribution caused by aging, the power command for ESMs in each arm is determined by:  
\[P_{\text{sc\_,}\_rj}^{*}=\frac{C_{\text{sc\_,}\_rj}}{C_{\text{sc\_,}\_tot}}P_{ \text{sc}}^{\text{ref}}+\frac{1}{2}\Delta P_{\text{sc\_,}\_j}+\Delta P_{\text{ sc\_,}\_rj}. \tag{26}\]  
In this case, submodule SOC balancing can also be achieved through the regulation of submodule capacitor voltage. This is because the supercapacitor SOC is determined solely by its rated voltage and actual voltage. Even when the capacitance value is affected by the aging of the supercapacitor, the relationship between SOC and voltage remains valid. Consequently, the strategy proposed in this paper remains effective in achieving SOC balance even under capacitance variations.
# Drone Deployment Algorithms for Effective Communication Establishment in Disaster Affected Areas
## 3 Proposed Methods
### Proof That Our Method Uses the Minimum Number of Drones  
To prove that the region-filling algorithm uses the minimum number of drones to cover the whole region, we show that the various stages in the algorithm ensure the minimum waste of coverage, and hence the minimum number of drones. The minimum wastage happens when the wastage is distributed equally in all directions.
# The Performance and Fouling Control of Submerged Hollow Fiber (HF) Systems: A Review
## 2 Submerged Membrane-Filtration Applications and Benefits
### Surface-Water Treatment  
The submerged membrane process is employed to remove microparticles and macromolecules, which generally includes inorganic particles, microorganisms, and dissolved organic matter (DOM) [34; 43; 44]. The microparticles and macromolecules present in the feed tend to affect the membrane pores adversely through pore blocking (i.e., sealing off the membrane pore entrance), pore constriction (i.e., narrowing the membrane pore channels), and/or cake-layer formation, all of which result in a decrease in the membrane permeability [35; 45; 46]. DOM 'particles', whose size approximates that of the membrane pores, can cause pore blocking, while microparticles and macromolecules larger than the size of the membrane pores result in a fouling layer on the membrane surface. Reversible fouling can be removed by hydraulic flushing/backwashing with air bubbles as scouring agents, whereas irreversible fouling binds more stubbornly to the membrane, thereby necessitating chemical cleaning [35; 36; 37; 47].
# Challenges, Threats, Security Issues and New Trends of Underwater Wireless Sensor Networks
###### Abstract  
With the advances in technology, there has been an increasing interest from researchers and industrial institutions in the use of underwater wireless sensor networks (UWSNs). Constrained by the open acoustic channel, harsh underwater environment, and their own particularities, UWSNs are vulnerable to a wide class of security threats and malicious attacks. However, most existing research into UWSNs has not taken security into consideration. Moreover, the existing relatively mature security mechanisms for WSNs cannot be directly utilized in UWSNs. For these reasons, this article aims to present a comprehensive overview of the particularities, constraints, attacks, challenges and current security mechanisms of UWSNs. In addition, challenging, open and hot research topics are outlined.  
underwater communication; underwater wireless sensor networks; security; UWSNs +
Footnote †: journal: Sensors
# Research and Implementation of CPS for Transmission Front Middle Case Assembly Line
## 4 IoT-Based CPS Framework  
### The Overall CPS Framework  
The overall CPS framework of the transmission front and middle case assembly line is shown in Figure 3. The main elements of this system include the actual physical assembly layer, the assembly process monitoring layer based on the IoT, and the cyber control layer.  
Figure 3: Overall CPS framework for Transmission front middle case assembly line.  
Figure 2: Process flow on stations of Transmission front middle case assembly line.  
The physical assembly layer includes all production factors of the assembly workshop, such as assembly workers, equipment, parts, and materials. This layer executes the commands of the system and feedback on the execution status to the cyber control layer of the system through the Internet of Things to ensure the management and control of the production status of the workshop by managers in real time.  
The monitoring system layer connects the physical assembly layer and the cyber control layer, and its main function is to collect and transmit process data based on Quick Response (QR) code, Integrated Circuit (IC) card, and other IoT technologies to realize the data connectivity from the physical world to the information world. It collects production status data of workers, equipment, parts, and logistics based on the assembly data model, then transmits these data to the cyber control layer to provide a data basis for informed decision-making.  
The cyber control layer includes all functional modules of the management and control system. Based on the assembly data model, it analyzes the process data collected by the IoT and makes decisions to realize the feedback and guidance of the information world to the physical world, such as the monitoring of the physical manufacturing process, discrepancy control, work order management, and so on. Through the cyber control layer, we can realize the effective control of the assembly process of transmission front and middle cases and improve the efficiency of the assembly line, as well as reduce the assembly cost.
## 2 Materials and Methods
### Experimental Design  
For the optimization of the SBSE conditions, a 2\({}^{4}\) two-level factorial design, using the statistical Computer Package Statgraphics Centurion 19.0 (Statpoint, VA, USA) was chosen. According to the bibliography [16; 23] and the authors' previous experience [24; 25], four  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Compounds** & **CAS Number** & **Retention Time (min)** & **Quantifying Ion** & **Qualifier Ions** \\ \hline Ethyl methyl sulphide (EMS) & 624-89-5 & 6.899 & 76 & 61, 48, 47 \\ Diethyl sulphide (DES) & 75-78-3 & 9.179 & 90 & 75, 47 \\ Thiophene (T) & 110-02-1 & 14.477 & 84 & 58, 45 \\
1-Pentanethiol (P) & 110-66-7 & 15.955 & 104 & 70, 55, 42 \\ Dimethyl disulphide (DMDS) & 624-92-0 & 16.931 & 94 & 79, 45 \\ Dipropyl sulphide (DPS) & 111-47-7 & 17.169 & 118 & 89, 43 \\ Diethyl disulphide (DEDS) & 110-81-6 & 23.026 & 122 & 94, 66 \\ Dibutyl sulphide (DBS) & 544-40-1 & 26.057 & 146 & 61, 56 \\ Dimethyl trisulphide (DMTs) & 3658-80-8 & 28.779 & 126 & 110, 79, 45 \\ Dipropyl disulphide (DPDS) & 629-19-6 & 29.711 & 150 & 108, 43 \\
2-Thiophenecarbovaldehyde (TC) & 98-03-3 & 39.778 & 112 & 111, 39 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Volatile sulphur compounds studied. Information employed for their identification and quantification.  
possible significant factors were selected: extraction time, temperature, alcoholic degree, and sample volume. Total chromatographic area and number of chromatographic peaks were fixed as experimental responses to be optimized. All the SBSEs were carried out using 1% EDTA, 10% NaCl, and agitation at 1100 rpm.  
This 24 design involved seventeen experiments performed in duplicate and in random order to provide protection against the possible effects of lurking variables. The values corresponding to the high (+) and low (-) points for each factor were as follows: 10 and 25 mL for sample volume; 30 and 100 min for extraction time; 25 and 35 \({}^{\circ}\)C for temperature; and 5 and 25% (\(\
u/v\)) for alcoholic content. A central point (65 min as extraction time, 30 \({}^{\circ}\)C as temperature, 15% as alcohol content, and 17.5 mL as sample volume) was also considered (Table 3).  
After determining the significant variables, a second experimental design (a rotatable central composite design (CCD) with \(\alpha=1.4142\)) considering only extraction time and alcoholic content was selected. Table 3 shows the levels for the different factors considered in each experimental design.
## 7 Experimental Verification
### Sparse Signal Transmission Performance  
Figure 22a shows the packet reception rate and recovery error comparison with varying transmitting distance. The distance area between the blue and green dotted link is the transitional region in the spatial characteristic of the lossy link; these two dotted lines respectively represent the 90% and 10% packet reception rates [28].We can see in the transitional region that using compressive sensing-based sparse signal transmission outperforms the transitional interpolation methods. This is because the compressive sensing method can recover the original sparse signal using only part of its sampled data, and the compressive sensing recovery algorithm is resistant to the noise in the signal.  
Figure 22b further shows that our methods expand the feasible communication range taking SMH application's requirement as an example [35]. The work in [35] states that the reconstruction error should be less than 30%, so as to satisfy the engineering requirement in practical the application of SHM. Therefore, next, we denote the recovery error threshold to be 30%, which means that the IAR equals one if the recovery error is less than 30%, and the IAR equals zero otherwise, as specified in Equation (11). In Figure 22b, we can see that when the transmitting power is \(-10\) dBm, the range of the connected region is around 7 m and the range of transitional region is about 19 m, which is from 8 m to 27 m. Using  
Figure 21: Spatial characteristic of the wireless link.  
traditional interpolation methods, one can only use the connected region for communication. While using compressive sensing-based methods, one can expand the feasible communication range from 7 m (connected region) to 23 m. The feasible communication range is promoted by almost 230%. This is only the result when the transmitted power is \(-10\) dBm, and we know that the larger the transmitting power is, the larger the relative range is between the transitional region and the connected region [28]. We can conclude that when the transmitting power is 0 dBm, which is practical, our method's superiority will become even more prominent.  
Figure 23: Mutual coherence and recovery error comparison with different packet lengths.  
Figure 22: Sparse signal transmission performance. (**a**) Packet reception rate (PRR) and recovery error comparison with varying transmitting distance; (**b**) PRR and information acquisition rate (IAR) comparison with varying transmitting distance.
## 6 Conclusions  
In this paper, a novel TF analysis method based on a reassigned spectrogram has been proposed to detect sweep interference for GNSS receivers. In order to prove the advantages and effectiveness of the developed technique, a comprehensive performance comparison has been carried out with the existed spectrogram and WVD approaches. The experiments have been performed on GPS L1 signals in the disturbing scenario in order to support the theoretical analysis among the aforementioned TF distributions used in interference detection for GNSS receivers.  
From the analysis results, the spectrogram approach presents the TF resolution trade-off problem and provides poor localization properties in the TF plane, since the analysis window is used, and in practice, it is not suitable to be used for detecting the disturbing term in the interfered GNSS signals; the WVD approach shows good TF resolution in identifying the characteristics of the interfering signal, but it presents a very severe cross-term problem due to the interaction of different signal components, which makes proper disturbance interpretation impossible.  
In order to eliminate the cross-terms and to preserve good time and frequency resolutions in the TF plane at the same time, a new TF distribution by using the reassigned spectrogram has been proposed in interference detection for GNSS receivers. This proposed technique efficiently combines  
Figure 12: ROC curves of the GPS L1 signal in different signal scenarios. The coherent integration time is 2 ms, \(C/N_{0}\) = 35 dB-Hz.  
the removal of the cross-terms provided by the spectrogram itself according to its inherent nature and an increased TF concentration of the auto-terms of the signal components achieved by the reassignment method. From the analysis results, the proposed interference detection method based on the reassigned spectrogram successfully solves the cross-term problem and, meanwhile, provides good readability and TF localization properties, showing much improved interference detection performance in comparison to the state-of-the-art TF analysis approaches.  
Moreover, the notch IIR filter has been adopted in the interference mitigation unit of the GNSS receiver providing significantly improved acquisition performance in terms of ROC curves in the presence of sweep interference in the received GNSS signals. The use of the notch IIR filter in the GNSS receiver has been proven to be valid and effective to improve the interference mitigation performance in jamming environments.  
The developed interference detection and mitigation techniques are very promising to be adopted in the design of an anti-interference GNSS receiver especially for civil aviation and military applications.