esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Quality Analysis and Improvement: A Case Study of a Bus Transportation System
## 6 Experimental Results and Analysis  
In this section, we first provide an overview of data quality issues across 12 different tables gathered within a real-world urban traffic big data platform. We secondly introduce our methods for cleaning redundant, conflicting, and erroneous data. Finally, we present solutions for filtering GPS noisy data and cleaning missing data. All methods are implemented in Spark, a large-scale data-processing engine, to handle datasets comprising hundreds of gigabytes.  
Figure 4 illustrates an overview of data quality issues across 12 different tables collected from a real-world urban traffic big data platform. This figure enumerates data quality issues, identifies the associated tables, and presents representative examples of data quality problems. The data quality issues encompass redundant data, conflicting data, erroneous data, noisy data, and missing data. As an example, we will use the \"Install_register\" table and the \"install_time\" field to illustrate a data quality issue. \"install_time\" is a timestamp that records information about when the operator deployed the data collection terminal on a bus. As we can clearly see from this figure, there are seven different time formats for the same value. On 16 November 2021, a group of construction workers installed numerous data collectors on various public buses. When entering data records into the database tables, seven different time formats were used. Specifically, three of them did not include information about the year. Various characters were used to separate the year, month, and day in the data. We can conclude that a flaw existed in the table design, as it lacked input content format checks, enabling unrestricted user data input.
# Spectrum Slicing for Multiple Access Channels with Heterogeneous Services
## 5 Non-Orthogonal Multiple Access
### PAoI-Oriented System
\[\omega_{i}(o)=\min(\mathcal{T}_{o}((i+1)N)\setminus\mathcal{T}_{o}(iN))-iN. \tag{52}\]  
If the number of groups \(G\) is larger than the number of slots in a frame \(N\), there might be no transmission opportunities in a frame; in that case, \(\mathcal{T}_{\omega_{i}(o)}(N)=\emptyset\), and the intermittent user will never transmit in that frame. For a given inter-transmission time \(z\), we can then define the number of frames without successfully received intermittent packets as \(M(z;d,o)\):  
\[M(z;d,o)=\left\lfloor\frac{z+d-1}{N}-1\right\rfloor. \tag{53}\]We can then give the pmf of the inter-transmission time if the next packet is not in the same frame:  
\[p_{Z}\big{(}z;d,o,L\big{)}=p_{F}(z-NM(z;d,o)+d;\omega_{M(zd,o)+1}(o))\prod_{i=0}^ {M(z;d,o)}\rho_{\varnothing}(\omega_{i}(o)). \tag{54}\]  
By unconditioning over \(L\) and \(d\), we get the pmf of the inter-transmission time:  
\[p_{Z}(z;d,o)= \begin{cases}(1-p_{L}(d;o))p_{Z}(z;d,o,L)&\text{if $d+z\leq N$;}\\ p_{L}(d;o)p_{Z}(z;d,o,L)&\text{if $d+z>N$.}\end{cases}  \tag{55}\]  
We can now join the results in (38), (49), and (55) to get the pmf of the PAoI for a given offset:  
\[p_{\Delta}(\tau;o)=\sum_{d=k+1}^{N}p_{R}(d;o)\sum_{w=0}^{\min(G-1,\tau)}p_{W}( w)\sum_{y=0}^{\min(\tau-w,d)}p_{Y}(y;d,o)p_{Z}(\tau-w-y;d,o). \tag{56}\]  
Finally, we uncondition on the offset \(o\) by considering all the possible offsets for a user. We assume that the initial offset is \(o_{0}\), and denote the set of reachable offsets from \(o_{0}\) as \(\mathcal{O}(o_{0})\). \[\mathcal{O}(o_{0})=\{o\in(\{1,\ldots,G\}\wedge\mathcal{T}_{o_{0}}(\infty))\}. \tag{57}\]  
The probability of having a random decoded packet be in a frame with offset \(o\) is then given by  
\[p_{O}(o;o_{0})=\frac{1-p_{\varnothing}(o)}{\sum_{o^{\prime}\in\mathcal{O}(o_{ 0})}1-p_{\varnothing}(o^{\prime})}. \tag{58}\]  
We can now uncondition the PAoI pmf:  
\[p_{\Delta}(\tau,o_{0})=\sum_{o\in\mathcal{O}(o_{0})}p_{O}(o;o_{0})p_{\Delta}( \tau;o). \tag{59}\]  
We remark that the grouping scheme is not necessarily fair to users, as users with a different initial index might have slightly different PAoI distributions.
# Multi-Scale Features for Transformer Model to Improve the Performance of Sound Event Detection
## 4 Experimental Results
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Evaluation Data**} \\ \hline  & \multicolumn{3}{c}{**Baseline Transformer**} & \multicolumn{3}{c}{**Multi-Scale Transformer**} \\ \hline  & \multicolumn{3}{c}{**F-Score (\%)**} & **ER** & **F-Score (\%)** & **ER** \\ \hline  & NEB = 1 & 29.68 & 1.80 & 33.13 & 1.75 \\ \cline{2-6}  & NEB = 2 & 34.89 & 1.69 & 33.83 & 1.69 \\ \cline{2-6} w/o & NEB = 3 & 29.29 & 1.76 & 35.81 & 1.61 \\ \cline{2-6} Processing & Average & **31.29** & **1.75** & **34.26** & **1.68** \\ \cline{2-6}  & Relative & \multicolumn{3}{c}{**-**} & **-** & **9.5\%** & **4\%** \\ \hline  & NEB = 1 & 36.36 & 1.17 & 39.32 & 1.10 \\ \cline{2-6} w/ & NEB = 2 & 41.07 & 1.11 & 39.91 & 1.11 \\ \cline{2-6} Post & NEB = 3 & 36.52 & 1.17 & 40.88 & 1.08 \\ \cline{2-6} Processing & Average & **37.98** & **1.15** & **40.03** & **1.10** \\ \cline{2-6}  & Relative & \multicolumn{3}{c}{**-**} & **-** & **5.4\%** & **4.3\%** \\ \cline{2-6}  & Improvement & \multicolumn{3}{c}{**-**} & **-** & **10.0\%** & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of event-based SED results on evaluation data between baseline Transformer model and multi-scale Transformer model. From Table 7, we can see that the proposed multi-scale Transformer shows better performance than the Transformer model in [25], as well as the baseline system of DCASE 2019, irrespective of using post processing (P.P) or not. Since the Transformer model used in our work has a similar network architecture as in [25], we can see that the performance improvement mainly comes from the use of the multi-scale features proposed in our work, although the results may be affected to some degree by the optimization methods used in each study. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Evaluation Data**} \\ \hline  & & \multicolumn{2}{c}{**Baseline**} & \multicolumn{2}{c}{**Multi-Scale**} \\  & & \multicolumn{2}{c}{**Mean-Teacher Model**} & \multicolumn{2}{c}{**Mean-Teacher Model**} \\ \hline  & & \multicolumn{2}{c}{**F-Score (\%)**} & \multicolumn{2}{c}{**ER**} & \multicolumn{2}{c}{**F-Score (\%)**} & \multicolumn{2}{c}{**ER**} \\ \hline  & NEB = 1 & 35.80 & 1.36 & 42.21 & 1.24 \\ \cline{2-6}  & NEB = 2 & 42.46 & 1.23 & 42.46 & 1.20 \\ \cline{2-6} w/o & NEB = 3 & 36.48 & 1.31 & 43.45 & 1.16 \\ \cline{2-6} Processing & Average & **38.25** & **1.30** & **42.71** & **1.20** \\ \cline{2-6}  & Relative Improvement & - & - & **11.6\%** & **7.7\%** \\ \hline  & NEB = 1 & 40.37 & 1.02 & 46.20 & 0.95 \\ \cline{2-6}  & NEB = 2 & 45.82 & 0.96 & 46.10 & 0.96 \\ \cline{2-6} w/ Post & NEB = 3 & 40.46 & 1.03 & 46.55 & 0.94 \\ \cline{2-6} Processing & Average & **42.22** & **1.00** & **46.28** & **0.95** \\ \cline{2-6}  & Relative Improvement & - & - & **9.6\%** & **5\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of event-based SED results on evaluation data between baseline mean-teacher model and the proposed mean-teacher model with multi-scale features.
# Efficient Lattice-Based Cryptosystems with Key Dependent Message Security
## Appendix A Proof of Lemma 7  
Proof.: The transformation \(T\) to access the distribution \(D\) over \(R_{q}^{*}\times R_{q}\), possibly \(bA_{s,\chi}^{*}\) or \(U\big{(}R_{q}^{*}\times R_{q}\big{)}\). Then, we prove it in two steps.  
The first step. Transformation \(T\) generates the sample \(\big{(}\overline{a},\overline{b}\big{)}\in R_{q}^{*}\times R_{q}\) by drawing from the distribution \(D\). When \(D=bA_{s,\chi}^{*}\), we have \(\overline{b}=(\overline{a}+k)\cdot s+\overline{x}\), where \(\overline{x}\leftarrow\chi\).  
The second step. To transform samples from \(D\) into samples from a different distribution. The sample \((a,b)\in R_{q}^{*}\times R_{q}\) from \(D\) will be transformed into \((a^{\prime},b^{\prime})\in R_{q}^{*}\times R_{q}\), where \(a^{\prime}=-\overline{a}^{-1}\cdot a\), \(b^{\prime}=b-\varphi+a^{\prime}\cdot\overline{b}\), and \(\varphi=(a^{\prime}+k)\cdot s+e_{1}\), \(e_{1}\leftarrow\chi\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Schemes** & **pk** & **sk** & **Ciphertext** & **Enc/Dec** & **KDM Security** \\ \hline ACPS & \(\widetilde{O}(n^{2})\) & \(\sigma\cdot\sqrt{n}\) & \(\widetilde{O}(n)\) & \(n\cdot\mathrm{polylog}(n)\) & Yes \\ RPKE1 & \(\widetilde{O}(n)\) & \(\sigma\cdot\sqrt{n}\) & \(\widetilde{O}(n)\) & \(\mathrm{polylog}(n)\) & Yes \\ RSKE2 & / & 1 & \(\widetilde{O}(n)\) & \(\mathrm{polylog}(n)\) & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison of ACPS and our schemes.  
Particularly \(a^{\prime}\in R_{9}^{*}\) is uniform due to \(\overline{a}\gets R_{q}^{*}\) is invertible modulo \(q\) and \(a\) chooses from \(U\!\left(R_{q}^{*}\right)\). If \(D=U\!\left(R_{q}^{*}\times R_{q}\right)\), then \((a^{\prime},b^{\prime})\) is also subject to \(U\!\left(R_{q}^{*}\times R_{q}\right)\). If \(D=bA_{s,\chi^{\prime}}^{*}\) then \(b=(a+k)\cdot s+e\), so we have  
\[b^{\prime} =b-\varphi+a^{\prime}\cdot\overline{b}=\left(a-a^{\prime}\right) \cdot s+(e-e_{1})+a^{\prime}\cdot(\overline{a}+k)\cdot s+a^{\prime}\cdot \overline{x}\] \[=\left(a-a^{\prime}\right)\cdot s+(e-e_{1})+\left(-a+a^{\prime} \right)\cdot s+a^{\prime}\cdot\overline{x}\] \[=\left(k-1\right)\cdot a^{\prime}\cdot s+a^{\prime}\cdot \overline{x}+(e-e_{1})\]  
Notice that we cannot obtain a reasonable distribution \(bA_{\varphi,\chi^{\prime}}^{*}\) set \(k=1\); in fact, the \(k\)-RLWE\({}^{*}\) problem remains hard. Then we have  
\[b^{\prime}=a^{\prime}\cdot\overline{x}+(e-e_{1})=\left(a^{\prime}+1\right) \cdot\overline{x}+(e-e_{1}-\overline{x})=\left(a^{\prime}+1\right)\cdot \phi+e^{\prime},\]  
where \(\phi=\overline{x}\) and \(e^{\prime}=e-e_{1}-\overline{x}\), therefore, \((a^{\prime},b^{\prime})\) is subject to \(bA_{\phi,\chi^{\prime}}^{*}\) as desired. \(\square\)
# Google Earth Engine for Monitoring Marine Mucilage: Izmit Bay in Spring Bay in Spring 2021
## 2 Study Area and Data  
The Marmara Sea, which is a semi-closed basin, has a two-layer water system formed as a result of the water exchange between the Black Sea and the Aegegan Sea. Since the salinity levels in neighboring seas are different, the salinity of the Black Sea waters flows in the thin layer on the surface of Marmara (0-25m), and below this layer, salty waters enter from the Aegegan Sea. Surrounded by highly urbanized and industrialized cities, the Sea of Marmara, located on the main transportation route is under high pressure from household and industrial pollution. For several decades poorly treated wastewater of mega cities has been discharged into the sea. As selected for this study, Izmit Bay, located in the east part of the Sea of Marmara (Figure 2), is under the constant pressure of pollution. The Bay has a weaker circulation compared to the other parts of the Marmara Sea [32]. The deepest point is 208 m, located in the central part of the Bay. The main reason for the low oxygen content in the Bay is a load of organic matter deposited from the upper layer and the oxygen consumed because of the metabolic activities of the organisms in the lower layer [2].  
Figure 2: Bathymetry of Marmara Sea [33].  
In this study, Sentinel-2 images acquired by European Space Agency (ESA) were used as the main data source. The Sentinel-2 mission started in 2015 and has a twin satellite constellation providing images at 5-day intervals. Both satellites carry Multi-Spectral Instrument (MSI) providing 4-bands at 10 m spatial resolution, 6-bands at 20 m spatial resolution, and 3-bands at 60 m spatial resolution with 12-bit radiometric resolution (Table 1). Radiometrically and atmospherically corrected Level-2A images acquired on May 14, 19, 24, and July 13, 18 and 28 having no clouds and June 13 having thin clouds were obtained from Sentinel's Scientific Data Hub ([[https://scihub.copernicus.eu/dhus/#/home](https://scihub.copernicus.eu/dhus/#/home)]([https://scihub.copernicus.eu/dhus/#/home](https://scihub.copernicus.eu/dhus/#/home)), accessed on 30 July 2021). It should be mentioned that June 2021 was generally cloudy and rainy, which makes optical satellite images impossible to be used in such a study. Due to these weather conditions and 5-day satellite acquisition frequency, cloud-free images could not be obtained for June 2021. However, for testing the effectiveness of considered indices in the delineation of mucilage-covered areas when thin or small clouds exist, an image acquired on 13 June 2021, was employed in this research.
## 6 Numerical Result of PLS  
In Figure 6, the trends in RSP are illustrated with respect to the source's transmission power and the untrusted relays, with the shape factor \(m\) being a parameter. The signal strength of the source, \(\mathcal{E}_{\text{S-UR}_{1}}\) and \(\mathcal{E}_{\text{S-UR}_{2}}\), and the signal strength of the relays, \(\mathcal{E}_{\text{D-UR}_{1}}\) and \(\mathcal{E}_{\text{D-UR}_{1}}\), are assumed to be the same, with the jamming signal strength remaining constant. Therefore, the S-UR\({}_{1}\), S-UR\({}_{2}\), UR\({}_{1}\)-D, and UR\({}_{2}\)-D links have the same average SNR, with the noise normalized to unity. The graph reveals an initial rise in the RSP, followed by a decline as the average power escalates, regardless of the fading severity (different values of \(m\)). This suggests that a mere increment in transmit power doesn't continually bolster the RSP. As the source's transmit power augments, the chances of the relays decoding the transmission accurately amplify, leading to a drop in the overall RSP. Peak RSP is observed when the source's and relays' transmit power contributions are in equilibrium.  
Furthermore, Figure 6 illustrates how varying the shape factor \(m\) affects the RSP in different Nakagami-m fading scenarios. With higher values of \(m\), representing less severe fading conditions, the RSP remains higher across a broader range of transmit power levels compared to lower values of \(m\). This indicates that in more favorable channel conditions, the network maintains a higher level of reliability and security on average. In contrast, under more severe fading conditions (larger values of \(m\)), the RSP decreases sharply, although it increases rapidly with increasing transmit power. Interestingly, regardless of the value of \(m\), the peak RSP occurs at the same transmit power level, indicating that the optimal point for maximizing reliability and security is unaffected by the severity of fading. This highlights the importance of optimizing power allocation and considering the channel conditions to maximize the reliable and secure performances of the network.  
Figure 7 demonstrates that to achieve the optimal RSP in a diamond network under Nakagami-m fading conditions, the majority of the transmit power, approximately 61%, should be allocated to the source, as indicated by the power allocation ratio \(a\). The remaining transmit power should predominantly be allocated to the untrusted relays, as indicated by the power allocation ratio \(b\), with only a very small portion allocated to the destination for jamming signals. This optimal power distribution ensures a balanced power allocation that maximizes RSP, highlighting the importance of strategic power allocation in designing efficient and secure communication systems resilient to jamming and fading effects.  
Figure 6: RSP versus signal strength (dB) of the source (S) and the untrusted relays UR\({}_{1}\) and UR\({}_{2}\). \(R_{\text{SUR}_{1}}=R_{\text{SUR}_{2}}=R_{\text{UR}_{1}\text{-D}}=R_{\text{UR} \text{-D}}=0.5\).
###### Abstract
Similarly, Fibonacci [66] and DIRECT MPPT [67]-based techniques have similar drawbacks, making them unsuitable for achieving global optimal values. ANN and FLC are two soft computing technologies that are knowledge-based systems that require specific knowledge to implement the algorithm. ANN and FLC are effective at tracking MPP, but they require a lot of memory to train and implement rules. The FLC method, in particular, has the significant drawback of being unable to modify the rules once they have been defined. Recently, many researchers have proposed hybrid MPPT methods and new optimization algorithms based on MPPT techniques. The ABC-P&O-based hybrid MPPT method decreases maximum overshoot by 30% when compared to the standard ABC-based MPPT method. However, the proposed method was tested with only two PV modules for PSCs [68]. A hybrid MPPT technique that included a particle swarm optimization approach and a cooperative ANN to maximize the PV system's output power was developed. The ANN predicts the quantity of solar insolation and cell temperature, whereas the PSO optimizes power generation and tracks the MPP of the PV system under changing climatic circumstances [69]. The authors in ref. [70] have proposed various MPPT methods based on the Bat algorithm, such as Bat assisted P&O, Bat assisted Beta, and Bat assisted INC. However, it has very slow convergence, i.e., slow tracking speed, and also produces more oscillations at a steady state. To address this, an improved grey wolf optimizer (GWO) to extract global MPP under partial shade was developed, which gives a faster convergence speed. However, initial oscillations and a long settling time are present [71]. In ref. [72], the authors presented an enhanced Math flame optimization (MFO) technique for obtaining maximum power under dynamic operating situations. This method improves tracking efficiency convergence speed. It is, however, more complicated, and it results in substantial initial steady-state oscillations.
# Optimal Steady-State Range Prediction Filter for Tracking with LFM Waveforms
## 5 Conclusions  
This paper proposed a gain design method to achieve optimal steady-state range tracking for the LFM \(\alpha\)-\(\beta\) filter. The RMS-index, which reflects the range prediction RMS errors in steady-state, was derived for the LFM \(\alpha\)-\(\beta\) filter, and the gain design based on the minimization of the RMS-index was presented. The theoretical analyses proved that the proposed method generates smaller RMS errors in range tracking compared with that for the conventional method, which is based on the minimization of the RMS estimation error and the Kalman filter equations. The relationship between range-tracking performance and the important parameters \(c_{\text{RD}}\) (the normalized range-Doppler coupling coefficient) and \(\Gamma_{\text{D}}\) (the design parameter called the deterministic tracking index) were also clarified. Moreover, numerical simulations employing LFM radar tracking verified the effectiveness of the proposed method. However, to clarify the performance of the proposed method in terms of the range prediction considering the range-Doppler coupling and the RMS-index, only theoretical analyses and ideal simulations were performed that did not account for various factors related to tracking accuracy, such as the multi-pass effect, signal distortion depending on the scattering characteristics of targets, directivity of the antenna, and multi-target cases. To apply LFM radar tracking with the proposed method in real environments, a comprehensive experimental study considering these foregoing effects and range-Doppler coupling is important work that remains.  
This work was supported in part by the JSPS KAKENHI Grant No. 16K16093. I thank [PERSON], Ph.D, from Edanz Group (www.edanzediting.com/ac) for editing a draft of this manuscript.  
The author declares no conflict of interest.
## 4 Performance of SERENA and TDMA/CA
### Simulations in an Ideal Environment for a General Application  
We first consider a general application and apply SERENA with three-hop coloring, as shown in Section 3. Figure 7 depicts the number of colors as a function of the number of nodes for different  
Figure 6: Example of: (a) No color conflict; (b) Color conflict.  
densities. We notice that the number of colors increases with network density and more slightly with the number of nodes. The increase with network density is expected because the neighborhood up to 3-hop increases with density and neighbors up to three hops do not share the same color. It appears that the impact of density on the number of colors is stronger with a higher number of nodes.  
Since TDMA/CA uses a time slot per color, the number of colors obtained with SERENA is equal to the number of time slots required in the active period. Figure 7 shows that except for 25 nodes with a density higher than 9, the number of slots used by TDMA/CA is considerably smaller than with TDMA, materialized by the first bisectrix. The benefit is around 48% for 49 nodes and a density 8 and reaches 71% for 100 nodes and a density of 8. We have also evaluated the average number of nodes sharing the same slot. It is higher than one in all configurations except this with 25 nodes and a density of 12. It reaches 3.5 for 100 nodes and a density of 8.  
We now consider the topology depicted in Figure 8 with 49 nodes and a density 8. In what follows, the duration of the cycle is fixed to \(4\) seconds. In the evaluation we did for the end-to-end delay, we considered the simple case where all color slots are equal. The duration of a color slot is fixed to \(0.128\) second. All the nodes of the network generate \(1\) message every \(5\) seconds. Each message contains 4 bytes of user data. First, we assume a general traffic, where destinations are randomly chosen. We consider the end-to-end delay as the difference between the time the packet is generated and the time it is received by the final destination. End-to-end delays are illustrated in Figure 9.  
In a general traffic scenario, the average end-to-end delay that a packet undergoes is \(12.84\) seconds (see Figure 9). It is interesting to notice that we have \(0\%\) of packet loss. Indeed, \(0\%\) is the expected result since the routing protocol used in these simulation runs uses only the links of good quality (having a received signal power of at least \(-86\) dBm).  
Figure 7: Number of colors in a general application.  
We now consider the case where each node sends traffic to a sink, denoted node 0. End-to-end delays obtained with the general coloring are depicted in Figure 10. In a data-gathering traffic scenario, the average end-to-end delay that a packet undergoes is \(8.53\) seconds.  
Figure 8: Topology of 49 nodes and a density 8 selected for delay evaluation.  
Figure 9: End-to-end delays for general traffic obtained with a general coloring.