esa-sceva/satcom-chunk-collection huggingface dataset:
# SMS Spam Detection System Based on Deep Learning Architectures for Turkish and English Messages
## 5 Results
Figure 6: Confusion matrix values obtained during the testing stage. that using the CNN algorithm, which delivers perfect performance in extracting n-gram features, together with the GRU algorithm, which is able to process word indexes and capture long-term dependencies, yields promising results in the detection of spam messages. The words frequently occurring within the datasets and the response times of the deep learning models during the test operation were also considered in the experimental studies. The words within the UCI SMS Spam and Turkish SMS Collection datasets are presented as a word cloud in Figures 8 and 9 according to the frequency of occurrence. The response times in the test operation, on the other hand, were recorded as 7.9 milliseconds for the CNN model, 7.4 milliseconds for the GRU model, and 6.6 milliseconds for the CNN + GRU model. Figure 8: Occurrence frequencies of words in the UCI SMS Spam dataset. Figure 7: ROC of proposed model. The CNN + GRU-based hybrid approach proposed in this article yielded the best results among all the algorithms and revealed its efficiency. The results obtained in the study demonstrate that a combination of deep learning algorithms is a promising solution for the classification of text messages and the detection of spam e-mails. It is observed that architectural engineering stands out in the models using deep learning algorithms while attribute engineering stands out in the models using machine learning algorithms. It is expressed in the literature that optimum results cannot usually be attained through deep learning algorithms if the dataset used is small in size. The hyperparameters to be used in a deep learning-based system must be tuned perfectly or the system must be designed in a way that yields the best result by generating the model from updatable structures.
# Hybrid Impedance-Admittance Control for Upper Limb Exoskeleton using Electromyography
## 3 Control System
### Admittance Control  
In the proposed system, the admittance control calculates the desired angle \(\theta_{x}\) which applying (3)  
\[\theta_{x}=\theta_{x-1}+k_{e}\times(S_{ag}-S_{an})-\cos(\theta_{x-1}+\theta_ {aj})\times K_{g}\times\frac{2}{s_{ag}} \tag{3}\]  
where \(\theta_{x-1}\) is the previously calculated angle, \(k_{e}\) is the electromyography constant, \(S_{ag}\) is the value of the normalized agonist signal, \(S_{an}\) is the normalized antagonist signal, \(\theta_{aj}\) is the angle of the anterior joint, and \(k_{g}\) is the gravitational constant. \(k_{e}\) defines a gain of relevance to the normalized electromyographic signal. Following biomechanics, a more developed musculature generates more strength than a person with less developed musculature. Thus, to perform the same movement, people with more developed muscles need to apply less force to obtain the same result. This coefficient adjusts the control according to the user's strength because the more muscle, the more is the myoelectric energy obtained by the EMG sensor.  
This model seeks to transform the electromyographic signal into motion. In the second term of (3), \(k_{e}\) increases or decreases the system's response to the signal, while \((S_{ag}-S_{an})\) determines the direction of movement. The third term of (3) seeks to support the arm's position after a movement made by the user. In this term, \(k_{g}\) assigns speed of response to the lack of electromyographic signals (\(S_{ag}\) and \(S_{an}\)), so the product \(k_{g}\times\frac{2}{s_{ag}}\) mimics the effect of gravity on the arm.
## 3 Results and Discussion
### Wettability of GN\({}_{4}\)/PSt Composites  
The wettability of porous materials towards water and oil is a vital indicator of their performance in oil/water separations [55]. As seen in Figure 5a,b, the WCA and OCA of the GN\({}_{4}\)/PSt porous composite were 151\({}^{\circ}\) and 0\({}^{\circ}\), respectively. To more clearly demonstrate the superhydrophobicity and oleophilicity of the prepared composite, water and oil were dropped onto the surface of the GN\({}_{4}\)/PSt monolith, and the water droplets remained as spherical shapes while the oil droplets were rapidly absorbed (Figure 5c). Under the application of an external force, a water droplet was forced to make contact and move on the surface of the material, and the water droplet moved easily and did not remain on the surface of the foam, suggesting that the water adhesion on the GN\({}_{4}\)/PSt composite was extremely low (Figure 5d). Moreover, the rolling angle (RA) of the material was small (RA = 8\({}^{\circ}\), Figure 5e). As shown in Figure 5f, the lightweight GN\({}_{4}\)/PSt porous material could be placed on a thin blade, and a water droplet placed on the surface easily rolled off at only a slight incline due to the low density and low RA of the prepared composite materials. An automatic mercury porosimeter was used to characterize the pore structure of GN\({}_{4}\)/PSt composites, and the density and porosity were 0.0276 g/cm\({}^{3}\) and 97.2%, respectively. The results indicated that the GN\({}_{4}\)/PSt foam had a hierarchical pore structure containing both nanopores (40-1000 nm) and micropores (1-20 \(\upmu\)m) (ESI; Figure S2), suggesting that the prepared material should be able to separate oil/water emulsions through a size sieving effect [56]. Moreover, the properties of the GN\({}_{4}\)/PSt porous sample were highly uniform, and the monolith could be randomly cut into various segments without affecting the shapes of the water droplets on any of the surfaces (Figure 5g). Figure 5h shows that the GN\({}_{4}\)/PSt porous material also had a strong water-impact resistance [57]. In summary, the addition of GN into an HIPE system resulted in GN\({}_{4}\)/PSt composite materials with superhydrophobic/superoleophilic properties as well as a low density and high porosity, making this material especially promising for the purification of oily sewage.  
Figure 4: Compressive stress–strain curve measured of the composite porous materials.
# Link Budget Analysis with Laser Energy for Time Transfer Using the Ajisai Satellite
## 3 Simulation Results
### Results
#### 3.2.2 Optimal Laser Energy
The TLTT link is available over the 30 degrees of satellite elevation from the receiving station, where the ratio of detection probability is near to 1 and its maximum values are 4.98, 4.36 and 3.68 for Geochang, Beijing and Koganei, respectively. These values can be interpreted as the maximum number of photoelectrons received at each station for the passage duration time of 5 msec. In these cases, the number of TLTT paths and averaged TLTT passage times per path at the Geochang SLR station are 86 paths (84% of total observable paths) and 6.5 min. These values are 78 paths, 5.6 min and 51 paths, 5.5 min for Beijing and Koganei, respectively, as shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Laser Energy**} & \multicolumn{3}{c}{**Number of TLTT Paths**} & \multicolumn{3}{c}{**Total TLTT Passage Times (min)**} \\ \cline{2-7}  & **Geochang** & **Beijing** & **Koganei** & **Geochang** & **Beijing** & **Koganei** \\ \hline
2.5 mJ & 0(0\%) & 0(0\%) & 0(0\%) & 0 & 0 & 0 \\
5.0 mJ & 48(47\%) & 33(33\%) & 15(16\%) & 238.3 & 104.4 & 26.0 \\
10.0 mJ & 61(60\%) & 50(50\%) & 34(36\%
# Estimation and Evaluation of Zenith Tropospheric Delay from Single and Multiple GNSS Observations
## 4 Results and Analysis
### ZTD from Multi-GNSS  
ZTD differences between four-system combined and other solutions at stations DGAR and PTGG in the year 2019 are displayed in Figure 6. Taking the four-system solution as a reference, it is simpler to see the improvement of ZTD's stability and discreteness from single-system to two-system, then to three-system solutions. In the single-system solution, it is evident that the GPS difference is the lowest and most stable relatively; the Galileo difference is larger than GPS, although a tiny quantity of the data are missing. The ZTD difference for GLONASS is larger than that for Galileo, with consistent data but larger fluctuations. The BDS difference has the greatest fluctuations and is the largest. ZTD differences at station DGAR show different levels of fluctuation. For example, overall ZTD differences are larger than 0 mm during the period of DOY 0\(-\)130 and smaller than 0 mm during the period of DOY 130-270. At DGAR station, ZTD estimation of multi-GNSS solutions cannot guarantee high accuracy at all times, but adding more satellite observations can indeed greatly improve accuracy and stability compared to the single-system solution. We also found significant fluctuations at DGAR station, DOY 282 for GE, GCE, and CER, possibly due to quality problems with Galileo's data in some cases.  
It can be clearly seen that the two-system solution has greatly improved when compared to the single-system solution. The difference for GPS and Galileo combined solutions is the smallest, while that for GPS and GLONASS is larger and more discrete. Overall, the three-system combined solutions present better results than two-system solutions, with the GCE combined solution being the best relatively, followed by the GER combined solution. Although the GCR combined solution is somewhat inferior, it is much better than GC and GR combined solutions. Probable reasons are that some observations are not yet available because of insufficient visible satellites and poor data quality in some cases. The RMS values for single-system (G, C, E, and R) are 3.0, 7.6, 4.0, and 4.2 mm; for two-system  
Figure 6: ZTD differences between four-system combined and single-system (G, C, E, and R) or multi-system combined (GC, GE, GR, GCE, GCR, and GER) solutions (**top panel**: DGAR; **bottom panel**: PTGG).  
(GC, GE, and GR) are 2.6, 1.5, and 2.3 mm; and for three-system (GCE, GCR, and GER) are 1.0, 2.2, and 1.3 mm, respectively. When there is data loss in a single system during certain time periods, a combination of multiple systems can compensate for this flaw, which means that in GNSS Meteorology research, receivers that can receive signals from multiple systems have significant advantages. High robustness and availability can be guaranteed for ZTD estimated from multi-GNSS fusion since the four-system ZTDs are more consistent and stable.
# Design of a Broadband and High-Isolation CMOS Active Quasi-Circulator
## 1 Introduction
The active balun and current combiner are utilized to attain a condensed form and facilitate straightforward impedance matching [12]. Nevertheless, these gadgets will diminish the available bandwidth. According to reports, a quasi-circulator that utilizes CMOS technology achieves high gain and excellent isolation. The quasi-circulator's operational bandwidth remains limited, and its power consumption is significant. However, the problem of limited operational bandwidth has not yet been resolved. The active distributed amplifiers provide a number of advantages, such as ultra-broadband, good gain flatness, and strong insertion gain [13]. These advantages make them suitable for wideband applications and provide good port-to-port isolation. A self-equalization strategy has been suggested for the active quasi-circulator [14]. The fundamental configuration comprises a distributed balun connected in series with a distributed combiner, both implemented using GaAs technology. A novel ultra-wideband quasi-circulator using the cascading distributed architecture implemented by 90 nm CMOS achieved high performance of ultra-wideband, good insertion gain, and small chip area, but the port-to-port isolation is not good [15]. Implementing a quasi-circulator in higher frequency, ultra-wideband, and high isolation using 90 nm CMOS technology is a significant hurdle. This study presents a novel quasi-circulator design that features a compact architecture primarily composed of three CS FETs. The quasi-circulator functions within the frequency range of C-band to K-band. Furthermore, the phase cancellation approach can be used to achieve higher isolation and broadband performance in \(|\)S31\(|\) for quasi-circulator applications. The circuit also achieves a good performance of power consumption compared with the state-of-the-art research. The subsequent sections of this work are structured in the following manner: Section 2 outlines the design concept, provides a thorough analysis of the detailed design for optimizing circuit parameters, and describes the execution of the suggested quasi-circulator. Section 3 presents the experimental findings. Section 4 concludes the text briefly.
## 6 AOG Downtime Reduction and Implementation Results
### Single Maintenance and Inventory Program (S-MIP)  
This system logs critical details such as the timestamp of requests, aircraft tail numbers, reasons for usage (scheduled maintenance or unexpected repairs), quantities withdrawn, and remaining stock. S-MIP also supports data-sharing capabilities through shared maintenance hubs with partner airlines, providing aggregated insights without revealing private details. These aggregated data include metrics like average daily demand for specific parts (e.g., hydraulic assemblies, avionics modules), lead times, and demand variability derived from forecast comparisons.  
Each air operator operates its own portal within S-MIP, but the system also offers an overarching view for enhanced operational efficiency. For instance, when multiple air operators, like KLM and Delta, draw from the same MRO stock, SmartLynx analysts can observe total daily usage patterns without knowing the exact breakdown per air operator. By identifying patterns, such as KLM's consistent ordering versus Delta's sporadic usage, SmartLynx can approximate each operator's daily demand for high-value and frequently moved parts. Similarly, lead times are inferred from aggregated data, allowing SmartLynx to estimate typical fulfillment times for competitors like Lufthansa.  
Moreover, S-MIP tracks demand variability by comparing actual usage against forecasts, enabling SmartLynx to assess whether competitor demand fluctuates significantly or remains stable.  
Figure 25: Correlation between safety stock and reorder point.  
Each row in the table shows how other operators' MTBF, demand variability (\(\sigma\)d\(\backslash\)sigma\(\_\)d\(\_\)ord), and lead times shape its safety stock and reorder points. By targeting a 90% service level (\(Z=1.28\)), carriers add a \"buffer\" on top of their predicted demand during lead time, meaning they hold just enough extra parts to avoid AOG issues if failures happen more often than expected. Lufthansa, dealing with high variability (8-10) and 6-8-day lead times, carries 9-14 units and enforces reorder points between 240 and 300, reflecting its need to protect long-haul flights. Delta, whose numbers fall between SmartLynx and Lufthansa, aimed to reduce its safety stock to 7-11 units by pushing its MTBF from 1215 to 1240 h.  
Consequently, as MTBF climbs, demand variability (the up-and-down swings of part failures) shrinks, letting logisticians lower the buffer without risking a stockout. Each air operator, however, must balance the expense of maintaining too many spares against the disruption of even a single AOG event, particularly on high-profile routes. By combining reliability improvements (MTBF) with precise demand forecasts (Holt-Winters) and service level targets (\(Z=1.28\)), these operators can keep just enough safety stock on the shelf to protect against downtime, yet not so much that they tie up unnecessary resources. This approach underscores the key takeaway: the more predictable your part failures and usage patterns, the fewer \"safety nets\" you need, all while preserving a high service standard in aviation.
## 2 Materials and Methods
### A-VTTCES-KF Algorithm
#### Innovation Sequence  
The innovation sequence, \(\mathbf{V}_{k}\), is defined as the difference between the latest observation and a priori estimation, which can be finally derived as follow [74]  
\[\mathbf{V}_{k}=\mathbf{H}_{k}\tilde{\mathbf{x}}_{k,k-1}+\mathbf{v}_{k} \tag{19}\]  
with \(\tilde{\mathbf{x}}_{k,k-1}=\mathbf{x}_{k}-\hat{\mathbf{x}}_{k,k-1}\), where \(\hat{\mathbf{x}}_{k,k-1}\) denotes the priori state vector estimation; \(\tilde{\mathbf{x}}_{k,k-1}\) is defined as the priori estimation error vector; \(\mathbf{v}_{k}\) stands for measurement noise vector. Then, the covariance matrix of \(\mathbf{V}_{k}\) can be given by  
\[\mathbf{\Gamma}_{k}=\mathbf{H}_{k}\mathbf{P}_{k,k-1}(\mathbf{H}_{k})^{T}+ \mathbf{R}_{k} \tag{20}\]  
where \(\mathbf{P}_{k,k-1}\) denotes the priori covariance matrix for the state vector. Ignoring the influence of the dynamic stress and other unintentional disturbance sources on the receiver measurements, i.e., pseudo-range and Doppler, thermal noise jitter will be adopted as the only random noise source for the measurements. Also, this type of the noise term can be quantified by a specific model which is dependent on the carrier-to-noise density ratio (\(C/N_{0}\)) due to the previous studies [42]. In other words, the measurement covariance matrix can be adaptively measured through the \(C/N_{0}\) estimations which are able to be obtained by the receiver design. Since this topic is not our primary contribution, it would not be discussed in this work. Then, on the condition that some approximations are as follow  
\[\mathbf{K}_{k}\approx\mathbf{K}_{k-1},\quad\mathbf{P}_{k,k-1}\approx\mathbf{ P}_{k-1} \tag{21}\]  
where \(\mathbf{K}_{k}\) is the Kalman gain, the covariance matrix for the process noise can be derived as  
\[\mathbf{\hat{Q}}_{k-1}^{-}\approx\mathbf{K}_{k-1}\mathbf{\hat{\Gamma}}_{k}( \mathbf{K}_{k-1})^{T} \tag{22}\]  
with  
\[\mathbf{\hat{\Gamma}}_{k}\approx\mathbf{H}_{k}\mathbf{P}_{k-1}(\mathbf{H}_{k })^{T}+\mathbf{R}_{k} \tag{23}\]  
Under this circumstance, the Kalman filter can be adaptively adjusted in terms of both process and measurement noise matrix. More detailed descriptions for the adaptive process covariance matrix can be referred to the forthcoming paper [75].
## 4 Results and Discussion
### RQ6: What Classification Models Are Used to Detect Web Vulnerabilities?  
The main objective of this research question is to identify DL models used for the classification of web attacks.  
In this section, we extract from the selected PS the classification models used for detecting web attacks. As seen in Figure 14, there exist different Deep Learning models with the exception of a few models such as Generative Adversarial Network and variants of Encoder-Decoders models. Few studies use Deep Learning models as feature extractors and Machine Learning (ML) algorithms as web attacks classifiers. This is why they were part of this study. Finally, we noticed that CNN, LSTM, and DFFN are the most Deep Learning models used in the reviewed studies.  
Since Generative Adversarial Network and Encoder-Decoder models have shown promising results in networks intrusion detection (e.g., [92; 93; 94; 95]), exploring these models in web attacks detection is an important area for improvement.
## 2 Sampling and Analysis Methods
### Meteorological Parameters  
Wind direction and strength of the wind, as well as the temperature regime, are important factors affecting the distribution of emissions in the air [57; 58]. Therefore, initially, when choosing reference stations, an analysis was made of the transfer of air masses from large stationary sources of atmospheric pollution (HYSPLIT model [[https://www.ready.noaa.gov/HYSPLIT.php](https://www.ready.noaa.gov/HYSPLIT.php)]([https://www.ready.noaa.gov/HYSPLIT.php](https://www.ready.noaa.gov/HYSPLIT.php)) (accessed on 16 January 2023)), and possible precipitation fields of atmospheric impurities (Figure 2) over the water area and coast of Lake Baikal were obtained. According to archival meteorological data (GDAS archive), for each day of the period (November-February), reverse trajectories of the movement of air masses were constructed. The duration of the trajectories was chosen to be 24 h. Calculations were performed for 250 m above ground level. Figure 2 demonstrates the distribution of air masses over Lake Baikal averaged over a six-year period: the main transfer pathway of air masses from stationary sources located in the Angara River valley (Irkutsk Region) was directed to the southeast coast of Lake Baikal (30-50%) and to the delta of the Selenga River (about 5%), which could contribute to a greater accumulation of anthropogenic impurities in the snow cover with a northwesterly wind direction. Transfer of impurities to the water area of Baikal from the industrial centers of the Republic of Buryatia occurred along the valley of the Selenga River in its delta (15%). For a more accurate visualization of the distribution of atmospheric impurities in the snow of the Southern Basin and the Selenga Delta, the sampling grid was made in more detail and was based on the calculations. The transfer of pollutants to the middle and northern basins of the lake, as calculations showed, was insignificant.  
The identification of groups of sources affecting the chemical composition of atmospheric fallout was performed in two stages. The first stage included an inventory of all possible natural and anthropogenic sources of aerosols in the region (Table 1). In the second stage, the natural and anthropogenic groups of sources affecting the chemical composition of atmospheric fallouts were determined by the methods of statistical processing of data from atmospheric fallout samples [59; 60], and the choice of tracer substances was made.  
Figure 2: Percentage distribution of transfer pathways of air masses (250 m above ground level (AGL)), calculated from the main source cities of the Baikal region during 2017–2022 (**A**) Irkutsk Region (**B**) Republic of Buryatia [[https://www.ready.noaa.gov/HYSPLIT.php](https://www.ready.noaa.gov/HYSPLIT.php)]([https://www.ready.noaa.gov/HYSPLIT.php](https://www.ready.noaa.gov/HYSPLIT.php)) (accessed on 16 January 2023).