esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results
### Network Set-Up Time Tests
#### 5.4.2 Results  
Table 5 shows the average network set-up time with a star working in Channel 21 and a beacon order of 9. Table 6 shows the standard deviation of the data that was operated to obtain each average time.  
The data in Tables 5 and 6 show that varying the super frame duty cycle does not significantly affect the average set-up time. However, there are significant variations regarding the standard deviation.  
With two stars working, a modification in the duty cycle generates variations in the average set-up time, which can be explained by the traffic increase in that channel. This, in turn, creates greater delays in the transition and reception of MLME-ASSOCIATE primitives. The longest set-up time observed was when the duty cycle was 100%.  
Table 7 shows the average set-up time with one star in the channel and a BO of 7. Table 8 shows the standard deviation of the data handled to obtain the average times of Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**BO = 9**} \\ \hline \multicolumn{2}{c}{**4 Nodes**} & \multicolumn{2}{c}{**6 Nodes**} \\ \hline
**SD/BO** & **Time (s)** & **SD/BO** & **Time(s)** \\ \hline
0.25 & 15.3270257 & 0.25 & 18.3955348 \\
0.5 & 23.2507967 & 0.5 & 9.54237951 \\
1 & 43.288385 & 1 & 11.99234 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Standard deviation of the set-up time samples. BO = 9.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**BO = 9**} \\ \hline \multicolumn{4}{c}{**4 Nodes**} & \multicolumn{2}{c}{**6 Nodes**} \\ \hline
**SD/BO** & **Time (s)** & **SD/BO** & **Time (s)** \\ \hline
0.25 & 39.3017952 & 0.25 & 44.121011 \\
0.5 & 36.0465694 & 0.5 & 24.4939538 \\
1 & 43.288385 & 1 & 90.7865608 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Average set-up time. BO = 9.  
When the duty cycle varies in a network with a BO of 7, there are greater differences in the average set-up time when compared with results in a network with a BO of 9. This is a consequence of, on some occasions, the reduced time available to transmit the association primitives. This causes some nodes to have to wait for the next beacon to successfully end their association process. This time variation was present in both one- and two-star network configurations.
# An Efficient Parallel Algorithm for Detecting Packet Filter Conflicts
## 3 Definition and Method of 5D Conflict Detection
### Definition of 5D Filter Conflict  
Conflict detection is relatively easier in 2D because it uses only two fields. If the conflict condition defined in [3] is extended to 5D conflict detection, it should compare five fields. When two filters, \(f=(f[1],f[2],\ldots,f[5])\) and \(g=(g[1],g[2],\ldots,g[5])\), satisfy the following two conditions, we can determine that filter \(f\) conflicts with filter \(g\).  
1. \(\forall_{1\leq k\leq S}(f[k]\cap g[k])\
eq\varnothing\).
2. \(\exists_{1\leq x\leq S}\exists_{1\leq y\leq S}((x\
eq y)\wedge(f[x]\subset g[ x])\wedge(g[y]\subset f[y]))\).  
Condition 1 indicates that when both filters \(f\) and \(g\) find any field \(k\) such that the set of matched results is empty, no conflict exists between filters \(f\) and \(g\). For example, when  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Filter** & **SA** & **DA** & **SP** & **DP** & **Protocol** \\ \hline \(F_{0}\) & \(101^{*}\) & \(01^{*}\) & \(*\) & 0–1023 & TCP \\ \(F_{1}\) & \(10^{*}\) & \(010^{*}\) & \(*\) & 80 & * \\ \(F_{2}\) & \(10^{*}\) & \(0^{*}\) & \(*\) & \(*\) & UDP \\ \(F_{3}\) & \(10^{*}\) & \(0^{*}\) & \(*\) & 0–1023 & * \\ \hline \hline \end{tabular}
\end{table}
Table 2: Example of a 5D filter database.  
Figure 3: Programming model of the CUDA.  
matching the protocol field of filters \(F_{0}\) and \(F_{2}\) (Table 2), the result is empty because no packet uses the transmission control protocol and the user datagram protocol simultaneously. In other words, when all fields \(k\), such as the matching result of \(f[k]\) and \(g[k]\), are not empty, \(f\) and \(g\) may have conflicts. If we can find fields \(x\) and \(y\) such that the results of matching filters \(f\) and \(g\) satisfy condition 2, we can conclude that \(f\) and \(g\) have conflicts. For example, all field matching for filters \(F_{1}\) and \(F_{2}\) are nonempty (Table 2), and we can always find DA and protocol fields such that \(F_{1}[\text{DA}]\subset F_{2}[\text{DA}]\) and \(F_{2}[\text{Protocol}]\subset F_{1}[\text{Protocol}]\). Therefore, \(F_{1}\) and \(F_{2}\) are conflicted.
## 3 Materials and Methods
### Comparison of the Signal before and after Denoising  
This section compares the histogram and probability density function (PDF) of the frequency scan signals obtained before and after denoising, as shown in Figures 47-54. In particular, Figure 47 gives the histogram and PDF of the site E signals collected at the 6 GHz band horizontal polarization. Figure 48 presents the histogram and PDF of the denoised site E signals at the 6 GHz band horizontal polarization. Figure 49 shows the histogram and PDF of the actual site F signals at the 6 GHz band horizontal polarization. Similarly, Figure 50 depicts the histogram and PDF of the denoised site F signals at the 6 GHz band horizontal polarization. Figure 51 gives the histogram and PDF of the site I signal at the 6 GHz band horizontal polarization in the same vein.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Sites** & **MAE\_Y** & **RMSE\_Y** & **MAPE\_Y** & **MAE\_Y** & **MSE\_Y\((\times 10^{4})\)** & **MAE\_F** & **RMSE\_F** & **MAPE\_F** & **MAE\_F** & **MSE\_F\((\times 10^{4})\)** \\ \hline A & 115.78 & 181.  
Furthermore, Figure 52 presents the histogram and PDF of the denoised site I signal at the 6 GHz band horizontal polarization. The histogram and PDF of the site J signals at the 6 GHz band horizontal polarization are given in Figure 53. Additionally, the histogram and PDF of the denoised site J signals at the 6 GHz band horizontal polarization are presented in Figure 54. Finally, the statistical analysis showing the performances of the links before and after denoising at 6 GHz in horizontal polarization is summarized in Table 12.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{Sites} & \multirow{2}{*}{\begin{tabular}{c} Mean \\ (10\({}^{-6}\)) \\ \end{tabular} } & \multicolumn{3}{c}{\begin{tabular}{c} Standard \\ Deviation \\ (10\({}^{-4}\)) \\ \end{tabular} } & \multicolumn{3}{c}{\begin{tabular}{c} Variance \\ (10\({}^{-6}\)) \\ \end{tabular} } & \multicolumn{3}{c}{\begin{tabular}{c} Interquartile \\ Range \\ (10\({}^{-4}\)) \\ \end{tabular} } & \multicolumn{3}{c}{\
\begin{tabular}{c} Kurtosis \\ (10\({}^{-4}\)) \\ \end{tabular} } & \multicolumn{3}{c}{
\begin{tabular}{c} Skewness \\ \end{tabular} } \\ \hline  & before & after & before & after & before & after & before & after & before & after & before & after \\ \hline A & 0.0047 & 1.2407 & 2.5080 & 0.2955 & 6.2901 & 0.0873 & 3.4110 & 0.
## 3 Materials and Methods
### Other Datasets for Ablation Study  
The proposed methodology is validated with two more WM datasets, and its classification accuracy is compared with traditional functional-connectivity metrics.  
Subtraction, music, and memory dataset (ablation-dataset-1):  
The dataset from the OpenNeuro repository contains EEG recordings from 62 healthy individuals who participated in 3 distinct cognitive exercises: subtraction, music, and memory [36, 37]. These activities are analyzed alongside resting-state EEGs, with eyes open and closed, to evaluate participants' cognitive function and resting state. The EEG data were collected using a 64-channel setup based on the international 10-20 system during the tasks, with a sampling rate of 500 Hz.  
* Memory Task: Participants are instructed to remember and recount all significant events from the day, from when they woke up until they arrived at the laboratory.
* Subtraction Task: Participants perform mental arithmetic by subtractively counting backward from 5000 in increments of 7.
* Music Task: Participants sing a song of their choice.  
These cognitive states--memory (Me), subtraction (Ma), and music (Mu)--are then compared with the resting states. Additionally, participants complete a questionnaire regarding each task to gather behavioral data. This information is used to conduct a three-class classification of the cognitive states mentioned above.  
Visual WM task (n-back task) (ablation-dataset-2):  
The dataset comprises EEG recordings from 20 healthy individuals engaging in the n-back task, as documented in studies [38, 39]. These recordings are compared against measurements taken during resting states. The EEG data, captured using a 256-channel setup in alignment with the 10-20 international system, have a sampling rate of 1000 Hz. The subjects, evenly divided between 10 males and 10 females, contributed bandpass filtered data from 3 to 45 Hz, with corrections made to remove artifacts like eye blinks and muscle movements.  
The dataset encompasses EEG data from four distinct activities:  
* Resting State: Subjects relax with their eyes open for 10 min.
* Visual Naming: Subjects name 80 distinct images and are also shown 40 scrambled images to which they should not react.
* Auditory Naming: Subjects listen to 80 different sounds and are required to identify each audibly.
* WM: Subjects are shown 80 images, including 40 previously displayed during the visual naming task, and must press a button to indicate recognition of repeated images.  
This research also explores a binary classification comparing resting-state vs. working memory tasks (n-back/0-back). Additionally, the study examines the effectiveness of various EEG channel selections (19, 21, 32, 256 channels) using a relief algorithm to optimize channel selection [40].
# An Indoor DFEC Ranging Method for Homologous Base Station Based on GPS L1 and BeiDou B1 Signals
## 2 DFEC Ranging Principle
### Cumulative Over-The-Cycle Detection  
When we use this ranging method, we must pay attention to one situation. When the phase difference value is accumulated over one cycle, the accumulated phase difference value may be larger than the wavelength of a signal having a higher frequency at a certain point. In this case, the higher frequency signal will appear distance compensation value for several cycles. At this time, we can combine information such as the signal-to-noise ratio, doppler, and the position of the previous moment to estimate the specific value of the compensation value added at the current position.
## 4 Flow of the Deep Mixing Project and QC/QA
### QC/QA for Stabilized Soil-Current Practice
#### 4.2.3 Quality Control during Production  
Stabilized soil columns must be produced to satisfy the specified geometric layout and quality of stabilized soil. The rig operator should control, monitor, and record the geometric layout of each column.  
As shown in Figure 5, the operation monitoring covers the quality and quantity control monitoring. The gauges and meters normally used are marked 1 to 8 in Figure 5.  
Quality control also includes binder storage, binder slurry preparation, and control of the mixing process. Storage and proportioning of binder, chemical additives, and mixing water are normally controlled, monitored, and recorded at the plant. Construction control parameters during column production include the penetration and withdrawal speeds, rotation speed of the mixing blade, flow rate of the binder, and water/binder ratio (for the wet method). The construction control values are predetermined by the process design considering the laboratory mix test result, field trial test, and contractors' experience. During column production, construction control values are controlled, monitored, and displayed in the control room for the plant and rig operators. The mixing shaft and blade should be frequently observed for any possible defects during production.  
The mixing degree mostly depends on the rotation speed and the penetration and withdrawal speeds of the mixing blade. An index named \"blade rotation number\", \(T\), has been introduced to evaluate the mixing degree, which means the total number of mixing blade passes during 1 m of mixing shaft movement, and is defined by Equations (3) and (4) for penetration injection and withdrawal injection. According to accumulated research, the \"blade rotation number\" should be approximately 270 or larger to ensure sufficient mixing for Japanese wet and dry methods [3; 7; 8].  
\[T=\sum M\cdot\left(\frac{N_{\text{d}}}{V_{\text{d}}}+\frac{N_{\text{u}}}{V_{ \text{u}}}\right)\text{ for penetration injection} \tag{3}\]  
Figure 5: Operation monitoring for the CDM method on land works (after [3]).  
\[T=\sum M\cdot\left(\frac{N_{\text{u}}}{V_{\text{u}}}\right)\text{ for withdrawal injection} \tag{4}\]  
where \(N_{\text{d}}\) = number of rotations of the mixing blade during penetration (N/min), \(N_{\text{u}}\) = number of rotations of the mixing blade during withdrawal (N/min), \(T\) = blade rotation number (N/m), \(V_{\text{d}}\) = penetration speed of the mixing blade (m/min), \(V_{\text{u}}\) = withdrawal speed of the mixing blade (m/min), and \(\Sigma M\) = total number of mixing blades.
## 3 Methodology
### Data-Frequency-Based Arithmetic Coding  
Our observations on ship status data reveal varying frequency distributions among values. For instance, in the \"Boiler Heavy Oil Density\" field, although values range from 825 to 845, a significant portion tends to hover around 835. Leveraging this insight, arithmetic coding algorithms, with the typical ideas assigning shorter encoding for frequently occurring values and longer encoding for occasional ones, are quite suitable to be applied for our ship status data. This approach compresseses data further while minimizing information loss, functioning as an almost optimal entropy encoding method [32].  
The challenge of applying arithmetic coding algorithm to our scenario lies in the fact that our maritime vessel state data can be viewed as a fixed-dimensional multidimensional vector, whereas arithmetic coding is typically utilized in symbol sequences of variable lengths. Given the diversity of fields in ship status data, each field necessitates an independent frequency table. Subsequently, the code vectors generated for all fields are combined into a single binary string--the final form transmitted to the receiver (typically the shore side). Notably, both sender (typically the ship side) and receiver share estimated data frequencies. During the encoding process, the frequency statistic tables remain static and are not dynamically updated or transmitted. Estimating frequencies for each value within a field is challenging directly, thus necessitating estimation from historical data. For every field in the dataset, the estimated frequency \(p_{i}\) of the \(i\)-th value is computed using the formula:  
\[p_{i}=\frac{f_{i}+1}{m+l} \tag{4}\]  
Here, \(m\) represents the total historical data entries, \(l\) denotes the possible values in the field, and \(f_{i}\) signifies the occurrence count of the \(i\)-th value in the data.  
Subsequently, the residuals obtained from the preceding predictive coding step are encoded using these frequency tables and transmitted to the receiver.
## 4 Results and Discussion
### Water Vapor Transmission Rate (Wvtr)  
Controlling the water loss from wound surface in order to prevent excessive dehydration is an essential factor for an ideal wound dressing [58]. [PERSON] et al. [59] reported that the water vapor loss rates for normal skin, granulating wound, and first-degree burn are 204 \(\pm\) 12, 5138 \(\pm\) 202, and 279 \(\pm\) 26 g/m\({}^{2}\)/day, respectively. A rate of 2000-2500 g/m\({}^{2}\)/day should maintain an appropriate moisture level without wound dehydration [60]. However, some commercial wound dressings, such as Geliperms (Geistlich Ltd., Switzerland) and Vigilons (Bard Ltd., Crawley, UK) have been reported to have a WVTR of 9009 \(\pm\) 319 and 9360 \(\pm\) 34 g/m\({}^{2}\)/day, respectively [61], which may dry the wound surface causing the dressing to adhere, requiring a secondary dressing to prevent water vapor loss. The calculated WVTR for the chitosan/alginate PEC films were in the range of 1900-2200 g/m\({}^{2}\)/day and in the ideal range for use as wound dressings (Table 2). WVTR values of TM-PEC %0.4 and TM-PEC %1 (\(p>0.05\)) were very similar. Incorporating TM-nanoemulsion decreased the WVTR (\(p>0.05\)). Regarding the data obtained, the prepared films have the potential capability to keep a sufficient level of moisture to build up exudates on the wound surface.
# Optimized Analytical-Numerical Procedure for Ultrasonic Sludge Treatment for Agricultural Use
## 3 How the Proposed Procedure Enables the Production of Sludge for Agricultural Uses  
The US technique tested in the Laboratory was integrated into an established sludge treatment process displayed in Figure 2 (whose steps are included in the red dashed box), while the proposed approach is highlighted with green hatching.  
Sludge from wastewater undergoes a first treatment line (water line) that deals with the purification of the water to remove contaminants and return it to the environment following regulatory standards. In contrast, the sludge line manages the sludge produced from the water line processes, aiming to stabilize it, reduce its volume, and valorize it, for example, through biogas production. The water line treats the main water stream, while the sludge line focuses on solid by-product management, working in synergy as sludge comes from the former's processes, and the latter's residues can be recirculated for further treatment. Specifically, the water line processes sludge from wastewater that undergoes initial screening through a screening battery that separates heavy parts (coarse sands and gravels), allowing the filtered sludge to undergo treatment to remove fats and oils. Then, an aerobic digestion chamber facilitates prolonged aerobic treatment (of about 17-20 days) and proceeds with appropriate sedimentation. The fraction of sludge that is insufficiently oxygenated is fed back into the aerobic digestion chamber to repeat the process, while the remainder undergoes NaClO-based treatment to reduce the presence of pathogenic microorganisms, bacteria, viruses, and other contaminants, making the sludge safer for the environment [59]. It is worth noting that in excess flow rates from biological, once the oils and fats are removed, the sludge goes directly to NaClO-based treatment. A further portion of the sludge exiting sedimentation constitutes the input to the sludge line, which, through gravity sludge thickening, aerobic biological stabilization, and sludge dewatering with belt filter press, produces the treated sludges (any supernatant is returned to the water line's head to be fed into the aerobic chamber). Then, before the treated sludge is considered waste (to produce dry sludge for agricultural use) at the end of the sludge line, the proposed procedure subjects it to the sonication process as already described so that the moisture percentage is significantly reduced by appropriate heat treatment. Finally, a monitoring system based essentially on moisture and temperature sensors sets the sonication parameters to optimize the whole process.
## 6 Results and Discussion
* The entire period and the seasons: After the visual inspection of Figures 5 and 6, we see that the EW distribution was closer to the empirical distribution than the other two distributions. This observation was supported by the results of the GOF tests and of the model selection criteria (Table 6), where the EW distribution obtained the highest value of \(R^{2}\) and the lowest values of all the other criteria, except for the winter. In the winter, the W3 distribution obtained better results for the information criteria. However, the EW distribution had the highest value of the coefficient of determination and the lowest values of the \(AD\) test and the \(RMSE\) among the discussed distributions. This indicated that the EW distribution demonstrated a better fit than the other two distributions. Thus, the EW distribution was more suitable for modelling the wind speed in this location for the entire studied period and for all the seasons. According to the criteria, W3 performed as the second best. We may conclude that for the considered datasets the EW distribution exhibited a significantly better fit to the wind speed data than the W2 and W3 distributions. It is obvious, that the EW distribution had superiority over the W2 and W3 distribution in modelling the peakness of the data. Further, because the EW distribution had larger right tail probability than the W2 and W3 distributions, this provided more flexibility for the EW distribution to model datasets where extreme strong winds or outliers in the direction of the right tail occurred. When we compare our findings with those in [49, 50], we can notice several similarities:
* The datasets in both other papers had high values of kurtosis--in the dataset from [49], the kurtosis was 2.502; in the datasets from [50], the values of kurtosis ranged from 3.877 to 8.806. The datasets here, with the EW distribution as the most suitable one, also possessed high values of the coefficient of kurtosis, ranging from 3.31 to 4.57. Figure 7: Modelled probability distributions fitted to the histogram of the wind speed data—the respective months. * The datasets in both other papers had positive values of skewness--in the dataset from [49], the skewness was 0.633; in the datasets from [50], the values of skewness ranged from 0.888 to 2.014. The datasets, modelled here, also had values of the coefficient of skewness ranging from 0.95 to 1.31. All of these datasets can be regarded as moderate to highly right skewed. Thus, the EW distribution can be used as a theoretical probability distribution for modelling the datasets of the wind speed with high positive skewness and kurtosis.
# Secure Mimo Communication System with Frequency Hopping Aided OFDM-DCSK Modulation
## 2 MIMO-FH-OFDM-DCSK System
### Transmitter
For \(\mathbf{E}_{m}\), the value of \(m\) depends on chaotic sequence of \(\mathbf{c}_{k}\), since \(0\leq k\leq\beta-1,\beta=N\), then have \(c_{k,m}>c_{k,q},0\leq q\leq\beta-1,m=m+1\), and the initial value of \(m\) is 0. Note that the initial value of chaotic sequences and the method of generating FH module are only shared between legitimate transceivers [18]. Similarly, for antenna 2, the same FH matrix is applied. Let \(\mathbf{F}_{2}\) represent the symbols obtained after \(\mathbf{G}_{2}\) hopping in antenna 2, then we have:  
\[\mathbf{F}_{2}=\mathbf{G}_{2}\cdot\mathbf{U}, \tag{8}\]  
where \(\mathbf{G}_{2}\) is the symbols modulated by DCSK, after conducting the chaotic modulation with the same chaotic sequences as the system with antenna 1. Subsequently, in the proposed scheme, the symbols that are modulated by FH in each antenna are modulated by OFDM using IFFT [15], and the expression is given as follows:  
\[\mathbf{T}_{1}=\frac{1}{\sqrt{N}}\sum_{n=0}^{N-1}\mathbf{F}_{1}\exp^{j\frac{2 m}{N}}, \tag{9}\]  
where \(\mathbf{T}_{1}\) represents the OFDM modulated symbols in antenna 1. These symbols are then sent to the antenna and passed into the channel.