esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Trajectory Data  
### PTDP Data  
The PTDP data used in this study are obtained from the GPS function of mobile devices and are provided by Agoop Corp. (Tokyo, Japan) [20]. Agoop Corp., the provider of these data, is a company that offers location-based services and is 100% owned by Softbank, one of the major telecommunications carriers in Japan. PTDP data are used for various purposes, such as urban planning, marketing analysis, and public policy development [39; 40; 41; 42]. Accurately estimating user groups, e.g., age groups, can significantly enhance the usability of PTDP data in these research areas, enabling more precise analyses and evidence-based decision making. The columns of the PTDP data table, which are used in this research study, consist of the following attributes:  
* Daily ID: User ID associated with the mobile device. This ID is reset on a daily basis.
* Timestamp: timestamp recorded in yyyy-mm-dd HH:MM for obtaining location information.
* Longitude: longitude of the location data.
* Latitude: latitude of the location data.
* Age group: age groups composed of those aged under 15, 70 and over and other age intervals divided into five-year increments, i.e., 0-14, 15-19, 20-24,..., 65-69, 70 and over.  
Each row in the PTDP data table records the above attributes, which represent a user's location information, demographic attributes, and time. For each user, daily location information is recorded across multiple rows. These point data are acquired through specific events, such as when a user opens an application on a smartphone. Each row of data is logged at a minimum interval of one minute. Therefore, by arranging these location data in a time series order, trajectories associated with individual users can be constructed.  
Notably, there are missing values for the age group attribute; however, in this study, we excluded data with missing age group information from the experiments. Using the age groups in their original form could result in an excessive number of classes, which might lead to an extremely small number of data points in each class. Moreover, the original classification does not clearly indicate the attributes of individuals in each class, such as whether they are students or workers. For these reasons, this study reclassifies the age groups into six categories: 0-19, 20 s, 30 s, 40 s, 50 s, and 60 s and over. These groups can generally be associated with standard attributes, such as students, early-career workers, young workers, middle-career workers, senior workers, and retirees. Notably, the behavior patterns of the 0-19 age group may overlap with those of their caregivers.
## 2 Materials and Methods
### PSAC Regeneration  
Regeneration and reuse of the adsorbent are essential to minimize the cost and maximize the potential of the adsorbent. In this work, regeneration was only performed for the black dye since, in previous work, only promising results were obtained for the black dye using hydrogen peroxide as a desorption agent, but effective desorption was not achieved for the red and blue dyes. Figure 9 and Table 6 show the results obtained for three adsorption/desorption cycles at different inlet dye concentrations. It is observed that both breakthrough time and adsorption capacity decreased with every cycle since active sites are lost due to a progressive degradation of the PSAC surface with the continuous operational conditions and the dye molecules that are irreversibly adsorbed [1; 44]. However, it was less significant for the lower inlet dye concentration.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**BTC Parameters** & **1 st Adsorption** & \multicolumn{2}{c}{**Cycle 1**} & \multicolumn{2}{c}{**Cycle 2**} & \multicolumn{2}{c}{**Cycle 3**} \\ \hline \(C_{0}\) & **25** & **100** & **25** & **100** & **25** & **100** & **25** & **100** \\ \hline \(q_{\text{bed}}\) & 146.5 & 130.4 & 45.8 & 30.9 & 36.2 & 37.4 & 32.0 & 47.3 \\ \% _Reneoval_ & 57.2 & 36.5 & 50.4 & 14.9 & 48.8 & 24.1 & 47.4 & 25.5 \\ \(V_{\phi}\) & 5087.9 & 3818.0 & 1955.1 & 2195.3 & 1555.0 & 1647.4 & 1402.0 & 1849.4 \\ \(t_{b}\) & 49 & 2 & 25 & 1 & 16 & 1 & 14 & 1 \\ MTZ & 1.8 & 3.9 & 1.8 & 3.9 & 1.8 & 3.8 & 1.8 & 3.9 \\ \hline \hline \end{tabular}  
\(C_{0}\) (mg L\({}^{-1}\)): initial dye concentration; \(t_{b}\) (min): breakthrough time; \(V_{\phi}\)(mL); volume of effluent; \(q_{\text{bed}}\) (mg g\({}^{-1}\)): adsorption capacity of the PSAC bed; \(MTZ\) (cm): mass-transfer zone.  
\end{table}
Table 6: Parameters obtained from BTC analysis for black dye adsorption on regenerated PSAC beds.  
Figure 9: BTC curves for the adsorption of black dye onto regenerated PSAC beds at different adsorption/desorption cycles (flow rate: 7.7 mL min\({}^{-1}\); bed height: 2 cm (**a,b**) and 4 cm (**c,d**); regeneration time: 4 h).  
Moreover, in every cycle, the volume of effluent needed for the experiment was significantly reduced while the \(MTZ\) was maintained, which means that the mass transfer is still occurring, and the adsorbent is still capable of adsorbing dye molecules. So, mainly at 25 mg L\({}^{-1}\), the PSAC possesses a good capability of regeneration since the reduction in adsorption efficiency only corresponds to 17%.
## 2 Related Work
### Modified TCN  
[PERSON] et al. proposed a two-channel TCN based on TCN(DD-TCN) for improving the accuracy of the regression prediction of mixed gas concentrations [32]. [PERSON] et al. adjusted the activation function and weight initialization of TCN(GL-TCN), and the model has a better fitting performance on high-dimensional time series datasets [33]. [PERSON] et al. adjusted the activation function and residual structure of TCN(Gaussian-TCN), and the model outperformed the traditional recurrent network in terms of prediction accuracy [34]. [PERSON] et al. proposed a multi-channel asymmetric structure prediction model based on the TCN for PM\({}_{2.5}\) concentration prediction in Fushun City, Liaoning Province [35].  
The methods proposed in this paper differ from previous approaches in the following aspects.  
* In this paper, the TCN is improved in more aspects. The activation function, residual structure, expansion rate, and feature-matching convolution position of the TCN are adjusted to make the LR-TCN model perform better.  
* The proposed LR-TCN is combined with other models to enhance the generalization ability of the model.
* Adaptive weights are used, which can be adapted to different datasets.
* Better generalization capability makes the model robust.
* Model complexity is reduced and less training time is required.
## 2 Related Works
### Concept Drift Detection Methods
HDDM [2] proposed two online non-parametric drift detection methods, HDDM_A and HDDM_W, based on Hoeffding bounds. HDDM_A is based on moving averages and is suitable for detecting abrupt drift; HDDM_W uses weighted moving averages to enhance the ability to handle gradual drift. McDiarmid Drift Detection Method(MDDM) [16] uses McDiarmid inequality to judge significant differences based on FHDDM. Experiments have shown that MDDM has a faster drift detection speed and lower false negative rate. WMDDM [17] is also based on McDiarmid inequality. This algorithm uses the sigmoid weighting function to implement instance weighting, and adjusts the adaptive factor to increase the weight after entering the warning level, thereby improving the speed of drift detection. Although WMDDM introduces the concept of dynamic weighting, its weight adjustment is based on the current phase, meaning that the weight coefficient increases with greater drift risk. While this strategy improves drift detection speed, it does not take into account the characteristics of different data streams. This aspect has not been addressed by current state-of-the-art methods. Therefore, providing personalized detection solutions based on data stream characteristics is a key focus of our research. Bhattacharyya distance-based Drift Detection Method (BDDM) [18] uses Bhattacharyya distance to detect concept drift. The author proposes a significance difference threshold suitable for weighted instances. Experiments have proved that BDDM is better than FHDDM in terms of accuracy and detection performance. Although BDDM demonstrates significant advantages on abrupt drift datasets, its performance in gradual drift environments still needs improvement. In our research, enhancing the adaptability of drift detectors in different data stream environments is also one of the key objectives. SeqDrift2 [19] uses Bernstein's inequality to detect concept drift. The author believes that Bernstein's inequality is suitable for low-variance environments. The literature [16] believes that SeqDrift2's assumptions about the data environment are too strict.
# Camera Geolocation Using Digital Elevation Models in Hilly Area
## 3 Difficulties in Locating Skyline Geolocation in Hilly Areas  
Compared with mountainous areas and deserts, skyline geolocalization in hilly areas has its own difficulties.
# Analysis of ICS and SCADA Systems Attacks Using Honeypots
## 6 Experiment
### Experimental Results  
To scan open ports on these honeypots, we used some tools; Nmap [18] is one such tool which searches for open ports in the target machine. Nmap has different scanning techniques, as summarized in Table 6.  
There are many scripting and advanced techniques used by Nmap for carrying out advanced and deep scanning for target machines. The above scripting is just an example. After scanning with these scripts, the honeypot's open ports are summarized in Table 7, and the ports' function can be described as follows. Port 80 is an HTTP port used to access Conpot through the web. SSH commonly uses port 22 to allow users to log in and issue commands on distant machines. Port 161 is used by firewalls, routers, and other hardware and software to utilize the Simple Network Management Protocol (SNMP) to send logging and management data to remote monitoring software. The Modbus protocol on port 502 is used for field device communication, while the ASF Remote Management and Control Protocol (ASF-RMCP) and the Intelligent Platform Management Interface (IPMI) Remote Management Protocol over UDP both officially use port 623. S7 Communication is carried out using port 102 (S7 comm). Programmable logic controllers (PLCs) of the Siemens S7-300/400 series communicate with one another via the proprietary S7 comm protocol.  
Shodan [19] is a search engine that obtains all connected devices' IP addresses and their banners. These devices can be routers, servers, switches, cameras, etc., and the banners might include information about the server's operating system, active programs, open ports, the features it supports, a welcome message, or anything else the client can learn before interacting with the server. Additionally, Shodan looks for any potential security holes in these systems, and accesses the exploits for such holes.
## 3 Techno-Economic Analysis at 2060
### Materials and Methods
To compute power productivity of these hypothetical turbines, we assumed (in a very rough first approximation) that the power curve is twice the curve of the reference 10-MW turbine. Therefore, performance calculation for this layout was simply run considering twice as many turbines. Another factor was the higher hub height of the supposed 20 MW turbine, which was set at 168 m a.s.l. Wind velocity at that altitude was calculated using the \"wind shear\" equation--\(\alpha\) equal to 0.11 (already discussed in Section 2). In order to outline the range of potential evolutions of floating wind LCoE in Italy, data inputs are clustered in different scenarios, highlighting optimistic, medium, and conservative inputs combinations:  
* \"Baseline 2030\", as already reported, considered the performance of 1000 MW reference farm without assuming any change in the resource compared to current values. This constitutes a reference point for following scenarios. * \"CC 2060\" (climate change at 2060) is a scenario that shows only wind resource rearrangement, without variations on cost and technology variables. Thus, modifying only wind speed inputs, climate change effects were isolated. * \"CC + TEC(I) 2060\" (climate change + low technology development at 2060) combines both climate change effects on wind velocities and an initial conservative hypothesis about costs and WACC (-10% for CAPEX and -5% for OPEX, WACC reduced from 7% to 5%). * \"CC + TEC(II) 2060\" (climate change + mid technology development at 2060) examines a more optimistic but still realistic framework in which, besides resource change, favorable reductions of WACC (reduced to 3.75%) and an increase in farm size (2000 MW) are assumed. * \"TEC(II) 2060\" (technology development II at 2060) simulates the same pattern of inputs of the previous scenario except for the fact that resource variation is not considered. * \"CC + TEC(III) 2060\" (climate change + high technology development at 2060) finally includes a wind farm operating over the most favorable techno-economic conditions: CAPEX and OPEX are assumed to decline by, respectively, 35% and 50%, compared to the 2030 baseline. WACC is fixed at 2.5%, a value corresponding to mature technology with established market.
## 4 Experiments and Results
### Experiment 2: Natural Images Test  
In this experiment, a more inhomogeneous artificial background is added to the two natural images with five levels of noise (SNR = 12, 14, 16, 18, 20) as shown in Figures 6a, 7a, 8a and 9a.  
The four natural images results achieved by different methods are shown in Figures 6b-f, 7b-f, 8b-f and 9b-f. Each column refers the results of a method; one to five refer to the results of PM, TV, CTAD, Malarvel, and our method. It is found that the noise can be reduced to some extent in different levels by each method. However, the deficiency in treating the inhomogeneous background prevented the four traditional methods from achieving satisfying results. The proposed model was found to be more successful in obtaining high quality results.  
PSNR and SSIM of the results are listed in Tables 3-6. It can be found that the proposed method achieves the best performance in each degradation level. Our method achieves significant improvements of PSNR (at least 9.97% in average on the Fruits image and 7.44% on the House image) for the well treatments on an inhomogeneous background. A similar situation can be found about SSIM; in fact, our results are at least 1.15% larger on the Fruits image and 1.14% larger on the House image than other methods. The advancement of the performance can be also found in tests on the Dogs image and Plane image.  
The iteration numbers are shown in Figure 10. Fewer iterations are required by our method.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**SNR** & **Metrics** & **PM** & **TV** & **CTAD** & **Malarvel** & **Proposed** \\ \hline
12 & PSNR & 29.3277 & 30.2468 & 30.0026 & 31.4023 & 33.3900 \\ \hline
12 & SSIM & 0.6158 & 0.7187 & 0.6825 & 0.73  
Figure 6: Results of Fruits image in Experiment 2.  
Figure 7: Results of House image in Experiment 2.  
Figure 8: Results of dogs image in Experiment 2.  
Figure 9: Results of plane image in Experiment 2.
## 3 Preliminaries
### Denoising Diffusion Probabilistic Models  
Denoising diffusion probabilistic models (DDPMs) have emerged as prominent generative models in recent years, recognized for their superior quality and diversity in image generation. The training process of DDPMs is more stable than that of GANs, and there are no problems such as mode collapse or mode drop. The algorithm consists of two processes: the forward process and the reverse process [20,21] (Figure 1).  
The forward process (from \(x_{0}\) to \(x_{T}\) in Figure 1) is the process of adding noise to the image. Given a real image sample (\(x_{0}\sim q(x)\)), the forward process adds Gaussian noise to it for \(T\) steps, resulting in \(x_{1},x_{2},x_{3}.....x_{T}\). The size of each step is controlled by a series of hyperparameters (\(\{\beta_{t}\in(0,1)\}_{t=1}^{T}\)) of the Gaussian distribution variance. Since each moment (\(t\)) in the forward process only depends on the moment (\(t-1\)), it can also be seen as a Markov chain process:  
\[q(x_{t}\mid x_{t-1}):=N\Big{(}x_{t};\sqrt{1-\beta_{t}}x_{t-1},\beta_{t}I\Big{)}, \tag{1}\]  
where the value of \(\beta\) varies with time. For example, \(\beta_{1}\) is small, \(\beta_{t}\) is large, and \(I\) is the identity matrix.  
The reverse process is the denoising inference process. If it is possible to reverse the forward process and sample from \(q(x_{t-1}\mid x_{t})\), it is possible to gradually approach the original image distribution (\(x_{0}\sim q(x)\)) from the Gaussian noise (\(x_{T}\sim N(0,1)\)). [PERSON] et al. [12] showed that if \(q(x_{t}\mid x_{t-1})\) follows a Gaussian distribution and \(\beta_{t}\) is small enough, \(q(x_{t-1}\mid x_{t})\) follows a Gaussian distribution. However, we cannot simply infer \(q(x_{t-1}\mid x_{t})\), so scholars use a deep learning model (U-Net) to predict such a reverse distribution (\(p_{\theta}\)):  
\[p_{\theta}(x_{t-1}\mid x_{t}):=N(x_{t-1}\mid\mu_{\theta}(x_{t},t),\sum_{\theta }(x_{t},t)), \tag{2}\]  
where the mean \(\mu_{\theta}\) and variance \(\sum_{\theta}\) are both calculated according to the model. By considering all time steps and according to the Markov chain, the reverse process can be derived, as shown in Equation (3):  
\[p_{\theta}(x_{0,T})=p(x_{T})\prod_{i=1}^{T}p_{\theta}(x_{t-1}|x_{t}), \tag{3}\]  
DDPMs use a U-Net neural network [22] for training (Figure 2). The network consists of downsampling layers, middle layers, and upsampling layers. In the downsampling  
Figure 1: Schematic diagram of DDPM training.  
layers, two residual blocks are inserted before each downsampling operation. The number of channels is doubled in each downsampling layer, except for the last step. The structure of the upsampling layers is symmetrical to that of the downsampling layers, but three residual modules are added before each upsampling operation. Unlike the downsampling layers, three residual blocks are used before each upsampling operation.
## 4 Results and Discussion
### Development of ML Models Using Building Register Information and Fire Scenarios
Figure 5: Confusion matrix of ML models (building register information and fire scenario). For both DT optimization (classifier) and RF optimization ML models, the prediction levels of the severe and low ratings were higher than those of the low ratings. For the validation models, the prediction level of the severe rating was 8-10% higher than that of the low rating. Overall, the KNN optimization ML model (classifier) exhibited a lower predictive performance than other models. In particular, the predictive performance of the validation model for the moderate and low ratings was lower than that of the training model. In addition, precision and recall were compared and analyzed to select a classifier learning model based on the values calculated from the confusion matrix. For the ANN, DT, and RF models, the precision and recall values for the moderate damage rating could not reach the average values, and those for the low and severe damage ratings were slightly higher than the average values. In the case of the KNN model, the precision and recall values for the low and moderate damage ratings could not reach the average values, and those for the severe damage ratings were slightly higher than the average values. Figure 5 emphasizes the importance of dividing data into training and test sets. If model training is performed only based on the entire dataset, satisfactory performance cannot be obtained for unknown data (for example, DT, KNN, and RF models yield lower recall for the test set compared to the training set).
## 4 Results and Discussion  
### Geological Significance of Resistivities and VES Categories  
The processing and analysis of the electrical boreholes made it possible to identify three categories of VES (C1, C2 and C3), characterizing the whole study area, based on the shape of the curve and the succession of electrical levels (resistant, conductive, two-layer intermediates, etc.). The table below gives a summary of the inversion results and the interpretation of typical VES in each category (Figure 5).  
The first category, C1, was the most dominant, containing a total of 30 VES measurements. A typical VES curve is represented by the 4P1 diagram (Figure 5). The interpretation of the latter is based on the lithological data from borehole 254/30. The diagram of VES4P1 shows, from bottom to top, the presence of the following geoelectric levels (Figure 5): (i) a relatively conductive bedrock with a resistivity of 170 \(\Omega\)m, located at a depth of 152 m and attributed to fairly compact Pliocene marlstone; (ii) a moderately resistant level of 450 \(\Omega\)m resistivity and a thickness of 75 m that can be made to correspond to water-bearing basalts; (iii) a very resistant complex formed by two levels, a first lower level of with a resistivity of 2043 \(\Omega\)m and a thickness if 55 m, which could correspond to dry and fairly compact basalts, surmounted by an upper level with a resistivity of 1347 \(\Omega\)m and a thickness of 21 m, most probably attributed to weathered basalts on the surface; and (iv) a superficial level that is 2 m thick, representing the vegetal soil.  
Figure 4: The methodological flowchart used in this study.  
the following geoelectrical levels: (i) a succession of moderately conductive to resistant levels, with resistivity varying between 40 \(\Omega\)m and 177 \(\Omega\)m, and thickness being 122 m, which corresponds to the marl-limestone intercalation of the Pliocene; and (ii) a 12 m thick resistant ensemble representing the Quaternary basalts, which were altered at the beginning, then dry before becoming slightly damp afterwards.  
The third category (C3) contained a total of 08 VES measurements. The curve of the electrical borehole 8P3 (Figure 5), which is the representative of this category, shows, from bottom to top, the presence of the following geoelectrical levels: (i) a very resistant substratum with a resistivity of 500 \(\Omega\)m. It is also very deep, located at a depth of 207 m, and could be the equivalent of Pliocene limestone and sandstone or any underlying formation (e.g., Jurassic limestones); (ii) a succession of moderately conductive to resistant levels, from resistivities of 50 \(\Omega\)m to 250 \(\Omega\)m, and 206 m thick, which would correspond to Pliocene marl and limestone; and (ii) a superficial conductive level, 1 m thick, which can be made to correspond to the vegetal soil.
# Measurement Grid Optimization for OTA Testing of 5G Smart Watches
###### Abstract  
Over-the-air (OTA) testing is crucial for optimizing wireless performance of 5G smart watches and improving their user experience. However, the current required test time is so long that it is almost impossible to complete the entire OTA testing without recharging and repositioning, which is unacceptable for the industry. Therefore, test-time reduction is significant. The objective of this work is to optimize measurement grids for OTA testing of 5G smart watches, which balance accuracy with efficiency. In this research, passive patterns from a typical 5G commercial smart watch are measured at different bands as reference patterns, which represent general radiation properties of 5G commercial smart watches. The effect of various coarse grids on OTA testing precision is characterized quantitatively by analyzing their accuracy in reconstructing reference patterns. The related measurement uncertainty (MU) terms are then evaluated and determined quantitatively based on statistical analysis. According to the derived MU limits for grid configurations, reducing grid points from currently required 62 (30/30) to 26 (45/45), and from 266 (15/15) to 62 (30/30) could save roughly 60% and 75% of the test time, respectively, with an uncertainty increase of 0.1 dB for both Total Isotropic Sensitivity (TIS) and Total Radiated Power (TRP) testing, which is considered acceptable. Furthermore, the feasibility of the proposed MU analysis and recommended grids have been experimentally verified.  
5G; smart watches; measurement grid optimization; MU analysis; OTA Article