esa-sceva/satcom-chunk-collection huggingface dataset:
# Weather-Aware Long-Range Traffic Forecast Using Multi-Module Deep Neural Network
## 3 New Traffic Forecasting Model Using Multi-Module Deep Neural Networks
### Iterative Traffic Predictor  
After the initial predictor performs its operations, the iterative traffic predictor is in charge of delivering all speeds and single-lane flows, for the time intervals \([t_{\text{start}}+R\Delta t,t_{\text{end}}]\), in a sequential manner. When the traffic flow of one time-window is determined, the predictor proceeds to the next window and accepts a new input. The input consists of date, time, weather, and link information, as well as the traffic data of the previous window. Notably, the speeds, single-lane flows, and link shifts (from link information) from previous windows are converted and rearranged as described below:  
1. The multi-lane flows (total flows of all lanes), single-lane densities, and multi-lane densities are computed using the speeds and single-lane flows.
2. The traffic data and link shifts are rearranged in a 2D (two dimensional) array by spatial and temporal order. 2D array displays the temporal and spatial correlation of traffic data well. Link shifts are used to reflect the distance between links. Figure 3 shows the rearranged 2D array of link \(x\) whose nearby links are \(x-1\) and \(x+1\), respectively.  
The 2D array is formed to reflect the road network connections; thus, link types are classified into  
* **Ordinary link**--A middle link connected to a single entrance and exit link,
* **Terminus link**--A link connected to local roads at the endpoint of a highway,
* **Interchange link**--A link having an additional connection of an on-ramp or off-ramp to a joining link  
Figure 3: Rearrangement of traffic data into 2D arrays.  
Figure 2: Structure of the initial traffic predictor.  
Figure 4 shows the structure of the predictor used in the iterative traffic computation. Compared to the initial traffic predictor, they have an additional module (2D CNN) that accepts the traffic data obtained in the previous window.  
Figure 5 introduces the detailed structure of the 2D CNN that extracts features from the previous traffic data, considering their spatial and temporal correlation. Inspired from [18], we split the ordinary convolution having \(2\times 2\) filters into \(2\times 1\) and \(1\times 2\) filters, named _Conv2d-space_ and _Conv2d-time_, respectively. They separately process the two parts to prevent interference of the information, resulting in better prediction performance. The results are combined into the single feature map on the _depth concatenation layer_ before being fed into the next convolution. At the _flatten layer_, the feature map is rearranged into a 1D array, which is suitable for the remaining fully connected layers. Finally, a series of fully connected layers remaps locally extracted features to yield global representations.  
Figure 4: Structure of iterative traffic predictor.  
Figure 5: Structure of 2D CNN accepting previous traffic data.
# Estimation of the Frequency Response Function of the Rotational Degree of Freedom
## 3 Vibration Analysis Using FEM  
### FE Modeling  
In this study, the accuracy of the estimation of rotational FRFs according to the measurement distance and finite difference order are compared. In addition, the rotation/moment FRF estimation accuracy by the compensation strategy was compared, and the accuracy according to the boundary condition was analyzed.  
Figure 4: Modal peak picking method.  
Figure 3: Two types of compensation strategy.  
Two or three response points were required to estimate \(L_{11}\) and \(N_{11}\). It is difficult to obtain and verify the rotational degree of freedom FRF through an experiment. Therefore, CAE was adopted to compare the rotational FRF estimation accuracy according to the distance (s) of the response point. In modal analysis using CAE, in order to reduce the effect error on the higher-order mode, the analysis should be performed considering at least 10 times the mode of interest or more. To solely consider the out-of-plane mode, modeling is performed using the B22 element model, which is a beam element of the 2D planar. The material properties of the beam model adopted for the vibration analysis are listed in Table 2, and the boundary condition is a free-free condition. To compare the accuracy according to the measurement distance, the response was analyzed by dividing the ratio of the total length of the beam. Figure 5 illustrates the position of the response point, which is the point for obtaining the rotational FRF. When the ratio of the distance of the measuring point to the total length of the beam is s/l, X01 represents s/l = 0.01. As such, X05, X1, X2, X3, X4, and X5 have s/l of 0.05, 0.1, 0.2, 0.3, 0.4, and 0.5, respectively. To verify the reliability of the vibration analysis of FEM, the FEM at some points and Euler-Bernoulli beam FRF results are compared. As illustrated in Figure 6, FEM modeling was verified as the results of the FEM and the theory are consistent.
## 3 Results and discussion
### Crystalline Phase, Spatial Homogeneity, and Crystallinity Degree of the Oxide
The trend observed in terms of photocatalytic activity improvement well correlates with the energy gap of the samples, which increases in the order TiO\({}_{2}\) (3.20 and 3.00 eV for anatase and rutile phase, respectively [28; 58]) \(<\) ZnO (3.37 eV [59]) \(<\) ZnTiO\({}_{3}\) (3.67 eV [48]) \(<\) Zn\({}_{2}\)TiO\({}_{4}\) (4.01 eV [60]). The smaller the gap, the more active the catalyst. The formation of high-gap ternary oxides (ZnTiO\({}_{3}\) and Zn\({}_{2}\)TiO\({}_{4}\)) is favored by the thermal treatment at 600\({}^{\circ}\)C, a temperature that allows removing the organic constituents from the precursors NFs and crystallizing the oxide nanocrystals. Indeed, Zn\({}_{2}\)Ti\({}_{3}\)O\({}_{8}\) and Zn\({}_{2}\)TiO\({}_{4}\) NRs have been obtained, upon calcination at high temperature, from ZnO NRs decorated with TiO\({}_{2}\) NPs prepared by hydrothermal method. Their formation has been attributed to the different diffusion rates of Ti\({}^{4+}\) and Zn\({}^{2+}\) ions [50]. Besides, the crystallinity degree of the sample also matters. [PERSON] et al. [61] have shown that the photocatalytic activity of ZnO nanoparticles synthesized via flame-spray pyrolysis improves with the crystalline quality of the oxide, as assessed via the XRPD analysis. The larger the average crystallite size, the higher the activity in the degradation of MB [61]. In the present case, based on the \(d_{\rm C}\) values inferred from the XRPD analysis (Figure 2a), the crystalline quality of the oxides improves in the order TZ11 \(<\) TZ12 \(<\) TZ21 \(<\) TZ01 \(<\) TZ10, i.e., consistently with the improvement order of the activity of the oxide NFs (TZ11 \(<\) TZ12 \(\cong\) TZ21 \(<\) TZ01 \(<\) TZ10). In the following, attention is focused on _a_-TiO\({}_{2}\) and ZnO NFs that outperform not only the Ti/Zn-oxide NFs, as above discussed, but also most state-of-art anatase and zinc oxide photocatalysts. This important feature is detailed in the next Section.
## 3 Results
### Bone Material Properties
#### 3.2.7 Validation  
To validate the results, the method used by most of the papers [17; 18; 20; 24; 25; 26; 27; 29; 31; 33; 35; 36; 37; 39; 40; 43; 44] is a comparison of the simulation results with the experimental results. The other method used by some papers [1; 2; 16; 19; 21; 23; 30; 31; 38; 41; 44] is a comparison of the results from the open literature. The experimental method involves a volunteer with similar characteristics to the population in the study, who is seated on a seat that is fitted with a pressure mapping system. The force-sensitive applications (FSAs) pressure mapping system now known as BoditTrak [51] was used by many papers [17; 27; 29; 40; 42; 43; 46], which included 16 \(\times\) 16 sensors, with a sensor size of 26.875 mm, a sample rate of 3072 sensors/s, calibrated pressure range between 0 and 200 mmHg, and an allowable stress of 26.7 kPa. Other pressure distribution measurement systems used were Tekscan [52], and Xsensor technology [53], which work similarly to the FSA pressure mapping system. The choice of using an experimental validation method or an open literature comparison method depends on the available resources and the overall environment of the test.  
The results from the experimental testing and the simulations show a good correlation with low percent errors, ranging from 2% to 9% error. It was noted that the papers that used a finer mesh with a high number of elements had a lower percentage of error, such as [PERSON] et al. [39], where the element size was low and the element type was a 4 node tetrahedron element, which had a percent error of 2 when compared to [PERSON] et al. [44] for validation. However, in the case of [PERSON] et al. [37], where the number of elements was lower, a percentage of error of 9% was noted, which was acceptable for the purpose of this study.
## 2 Localization Solution
### System Overview  
Figure 1 illustrates the overall setup of our ultrasound-based localization system. Ultrasound beacons are mounted on the ceiling to provide consistent coverage and minimize  
obstructions. The mobile robot, equipped with ultrasound receivers, moves within the area covered by the beacons. This configuration allows for accurate positioning of robots with varying heights, as the system can account for the vertical offset between the beacons and the robot-mounted receivers.
## 3 Results
### Evaluation Metrics  
The commonly used image quality evaluation metrics include PSNR, SSIM, NIQE [19], visible edge gradient method [20], RRPD [21], LIFQA [22], and DeepSRQ [23], etc.  
In the experiment, as we do not have corresponding reference clear images, in order to objectively demonstrate the dehazing effect, we used the widely used visible edge gradient method and NIQE to evaluate the experimental results. The visible edge gradient method has two important indicators: the ratio of visible edges \(e\) and the regularized mean visible edge gradient \(\overline{r}\). Visible edges are assumed to be those with local contrast greater than 5%. As shown in Figure 5, after defogging, the overall contrast of the image is enhanced and the number of visible edges measured increases.  
The relevant expressions are as follows:  
\[e=\frac{n_{r}-n_{0}}{n_{0}} \tag{8}\]  
Figure 4: Some sample images from the icing wind tunnel. ((1–6) in the picture represent Image 1 to Image 6).  
Figure 5: Comparison of visible edges before and after dehazing: \(e=0.88,\overline{r}=2.91\) after dehazing.  
\(n_{0}\) and \(n_{r}\) represent the number of visible edges before and after image defogging, respectively, and e represents the ability of the algorithm to restore invisible edges in the image. The larger the value, the better the defogging effect.  
\[\overline{r}=exp(\frac{1}{n_{r}}\sum_{P_{i}\in Y_{r}}\log r_{i}) \tag{9}\]  
\(Y_{r}\) is the set of pixels on the visible edges of the image after dehazing, \(r_{i}\) represents the ratio of gradients at pixel point \(P_{i}\) between the defogged image and the foggy image. The larger the value of \(\overline{r}\), the higher the contrast of the image after defogging, and the better the effect.  
\(NIQE\) calculates the quality score of an image by comparing the statistical properties of the image with those of a high-quality natural image reference set. The expression is as follows:  
\[D(v_{1},v_{2},\sum_{1},\sum_{2})=\sqrt{((v_{1}-v_{2})^{T}(\frac{\sum_{1}+\sum _{2}}{2})^{-1}(v_{1}-v_{2})} \tag{10}\]  
where \(v_{1}\), \(v_{2}\), \(\sum_{1}\), and \(\sum_{1}\), respectively, represent the mean vectors and covariance matrices of the natural MVG model and the distorted image MVG model. A smaller \(NIQE\) value indicates better perceived quality.  
Due to the defogging operation on the picture in the icing wind tunnel environment, it is necessary to preserve the structure and edge information of the icing wing in the picture to a great extent. So, we pay more attention to the value of the visible edge ratio \(e\) and the regularized visible edge gradient mean \(\overline{r}\). The \(e\) and \(\overline{r}\) values can more objectively show the number of edges recovered in the image after dehazing.
## 5 Conclusions  
This study evaluated the effect of wearing exoskeleton devices on the lower limbs by measuring EMGs and subjective discomfort ratings. The CEX and Chairless Chair are wearable devices that the participant can use to stand or sit while performing a task. Results showed that the muscle activity of the lower limbs (retcus femoris, biceps femoris, and tibialis anterior) decreased significantly when the participant wore the exoskeleton device at different working heights. However, wearing the exoskeletons had different effects on the upper limbs (upper trapezius and middle deltoid) depending on the working height. When the participant used the exoskeleton device, the upper-limb muscle activity significantly decreased at the lowest working height (60 cm), increasing at 85 and 110 cm. Therefore, when considering the physical workloads of the upper and lower limbs, the exoskeleton device is most effective when applied to a height of 85 cm or less.  
The most important implication of this study is that the effects and side effects of wearing an exoskeleton device vary depending on the working height and muscle type. Lower-limb muscles showed positive effects when worn at all three working heights, while upper-limb muscles showed different effects depending on the working height. This implies that the upper and lower limbs should be fully considered to introduce the exoskeleton. In addition, the task's characteristics, such as the working height and working type, should also be considered when introducing exoskeleton devices in the industrial field.  
The recommendation of wearing a lower-limb exoskeleton at a working height of 85 cm or less may apply to manufacturing workers who perform assembly tasks that burden the knees and legs, such as automobile assembly lines with repeated and improper body postures due to low working heights. In addition, as mentioned in the Introduction, the risk of WMSDs in the agricultural sector is higher than in other sectors due to the use of squatting and knee-bending working postures during the harvesting of peppers, eggplants, and watermelon at a low working height. This study suggests that wearable lower-limb exoskeletons in agriculture sites can reduce the workload on farmers' lower and upper limbs and reduce the incidence of musculoskeletal disorders.  
Conceptualization, H.-H.S. and Y.-K.K.; Methodology, K.-H.C. and H.K.; Data curation, S.S. and M.-T.S.; Writing--review & editing, J.H.K. and D.P.; Visualization, S.-Y.K. All authors have read and agreed to the published version of the manuscript.  
This work was carried out with the support of the \"Cooperative Research Program for Agricultural Science and Technology Development\" [Project No. RS-2022-RD010384 (PJ01709901)].  
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of Sungkyunkwan University (IRB# SKKU-20210107).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 2 Related Work
The system employs this combined structure to detect static network traffic patterns, along with dynamic behaviors, in IoT systems because accurate botnet recognition of stealthy and changing attacks is essential. The model obtains evaluation results via authentication with the Bot-IoT dataset, representing a benchmark resource for IoT botnet attack detection. Experimental findings show enhanced performance relative to standard machine learning approaches, with superior accuracy rates (greater than 99%) and improved precision, recall, and F1 scores. The system demonstrates a capacity for detecting false alarms effectively, which is an issue that IDSs normally struggle with. The proposed model adapts well to real-time IoT environments with restricted resource availability, in addition to exhibiting robust scalability features. The authors reported substantial progress in botnet detection methods, integrating deep learning architecture capabilities and solutions for single-model weaknesses. In addition, the abovementioned research provides an influential and intelligent IDS framework that uses hybrid deep learning to lead the way in securing future IoT networks against advanced botnet threats. However, the use of deep learning for botnet attack detection is associated with problems in terms of both scalability and real-time monitoring within IoT networks, which operate with constrained resources. Real-world deployment of deep learning models is difficult because they require large amounts of training data; however, the effective deployment of such data might prove difficult.
## Materials and Methods
### Characterization of Materials  
SEM (scanning electron microscopy) observations were performed on a Phenom ProX SEM (Thermo Fisher Scientific, Eindhoven, The Netherlands) at an accelerating voltage of 15 kV. The chemical composition of the samples was characterized by energy dispersive X-ray analysis (integrated to Phenom ProX SEM).  
BET surface area and porosity of the sorbent were recorded by Micromeritics TriStar II (Norcross, GA, USA) system at 77 K and using the BET equation with N\({}_{2}\) gas and desorption branches of isotherms based on BJH methods, respectively. BET samples were dried for 4 h at 120 \({}^{\circ}\)C with N\({}_{2}\) gas before testing (final degassing was performed again at 120 \({}^{\circ}\)C under vacuum).  
The thermogravimetric analysis of materials was performed using TG-DTA equipment (Netzsch STA 449 F3 Jupiter, NETZSCH-Geratebau HGmbh, Selb, Germany); analysis was carried out under N\({}_{2}\) atmosphere conditions with a temperature ramp of 10 \({}^{\circ}\)C min\({}^{-1}\) (and a 1 min step at 100 \({}^{\circ}\)C).  
FT-IR spectrometry analysis was recorded using an IRTracer-100 FT-IR spectrometer (Shimadzu, Tokyo, Japan). All the samples were dried at 60 \({}^{\circ}\)C before being analyzed. Samples were conditioned as a KBr disk containing 1% (_w/w_) finely ground material particles.  
XPS spectra were recorded using an ESCALAB 250 XI\({}^{+}\) instrument (Thermo Fischer Scientific, Inc., Waltham, MA, USA) with monochromatic X-ray Al K\({}_{\alpha}\) radiation (1486.6 eV) and the following operating conditions: the spot size was 500 \(\mu\)m; the sample-preparation pressure was set to 10\({}^{-8}\) mbar. The calibration of the energy interval was set on Ag 3\(d_{5/2}\) signal (\(\Delta\)BE: 0.45 eV) and C 1s signal (\(\Delta\)BE: 0.82 eV). The full-spectrum pass energy and narrow-spectrum pass energy were set at 50 eV and 20 eV, respectively.  
The pH-drift method was used for the determination of the pH of zero charge (pH\({}_{2\mathrm{PC}}\)) [42]. A series of solutions were prepared with the initial pH (pH\({}_{0}\)) varying between 1 and 11 using NaCl as the background salt (at the concentration of 0.1 M). The material was shaken for 48 h with the different solutions with a sorbent dosage of 2 g L\({}^{-1}\). The sorbent was filtered from the solution, and the equilibrium pH was measured using an S220 Seven Compact pH/ Ionometer (Mettler-Toledo Instruments, Shanghai, China). The equilibrium pH was measured and compared to the initial pH value. The pH\({}_{2\mathrm{PC}}\) was defined by the pH value verifying pH\({}_{0}\) = pH\({}_{\mathrm{eq}}\).
## 4 Design of Dynamic Cache Allocation Mechanism
### Normalized Weight and Cache Allocation  
According to Equation (5), the NACK weight, distance weight, and rate weight are respectively multiplied by the corresponding weight coefficients \(\alpha\), \(\beta\), and \(\gamma\), and then they are added together. Then the cache weight \(W_{i,n}\) of the multicast group \(i\) in the \(n\)-th statistical period can be calculated.  
\[W_{i,n}=\alpha\times N_{i,n}+\beta\times D_{i,n}+\gamma\times R_{i,n} \tag{5}\]  
where \(0\ \leq\alpha,\beta,\gamma\leq 1\), and \(\alpha+\beta+\gamma=1\). The size of the weight coefficients reflects the relative importance of the corresponding weight parameters in the reliable multicast cache allocation. Since different multicast applications have different emphasis on network performance indicators, the cache weight corresponding to each multicast group can be calculated by adjusting the weight coefficient corresponding to each weight parameter.  
In order to allocate all the available cache space to the multicast group, we normalize the cache weight of each multicast group, and the final weight \(QW_{i,n}\) of the cache quota for multicast group \(i\) is  
\[QW_{i,n}=W_{i,n}/\sum_{j}^{M}W_{j,n} \tag{6}\]  
where, \(QW_{i,n}\) of each multicast group satisfies \(\sum_{i}^{M}QW_{i,n}=1\). \(QW_{i,n}\) reflects the proportion of the cache space that the multicast group can obtain in a multicast tree node.  
Let the total available cache resources be \(C\) (in units of chunk). According to Equation (7), the cache size allocated to multicast group \(i\) in the \(n\)-th statistical period (represented by \(C_{i,n}\)) is  
\[C_{i,n}=\ \lfloor QW_{i,n}\times C\rfloor \tag{7}\]  
where, \(\lfloor.\rfloor\) represents the floor operator.
# Optimized Polarization Encoder with High Extinction Ratio for Quantum Key Distribution System
## 3 Results  
We simulated the relationship between secure key rate and distance using the two-decoy-state method and found that our QKD system can achieve a maximum communication distance of 150 km (see Figure 3). Across each distance, a total of \(N=10^{10}\) pulses is sent out. The intensities of the signal light is set to \(\mu=0.56\), and decoy states are simulated as \(\
u_{1}=0.23\) and \(\
u_{2}=0\). We achieved a secret key rate of 3 kbps at 100 km in experiments (see Figure 3). We compared the secure key rates of Peng [35], Ma [32], and our QKD scheme and found that our QKD scheme has a clear advantage in terms of key rate. To achieve a secure key rate of Mbps, the frequency of the light source should be increased to 1.25 GHz, and an SNSPD with a free-running dark count rate of 500 Hz and a quantum efficiency of 85% should be used. With these parameters, a secure key rate of 12 Mbps can be achieved at a distance of 50 km.  
Our SCPE produces four linear polarization states of \(|H\rangle,|V\rangle,|D\rangle,|A\rangle\), instead of \(|L\rangle,|R\rangle,|D\rangle,|A\rangle\), simplifying the QKD system. Additionally, most Sagnac polarization modulators require custom devices, while our improved SCPE can be constructed using only commercially available off-the-shelf (COT) devices, reducing cost and increasing convenience. Table 1 provides a detailed comparison of the performance of our QKD system with other QKD systems.  
Figure 3: The key rate performance of SCPE. The experimental rate distance performance of SCPE, compared with the theoretical simulations, along with Peng [35] and Ma [32].