esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Material Characterization  
The analysis of XRD data revealed that the main phase in the initial powder was BaTiO\({}_{3}\) in tetragonal modification (COD #96-152-5438) (Figure 2a,b). Calculated cell parameters were as follows: c = 4.02287 A, a = b = 3.99337 A; tetragonality c/a = 1.0074. Besides barium titanate, minor phases of barium zirconate (BaZrO\({}_{3}\), COD #96-153-8370), zirconium oxide (ZrO\({}_{2}\), COD #96-210-8454), and barium carbonate (BaCO\({}_{3}\), COD #96-100-0034) were found in the powder. The appearance of zirconium oxide and zirconate phases, which were not presenting in the as-purchased powder, was attributed to the milling procedure. The tetragonal BT has a density of 6.02 g/cm\({}^{3}\) and a melting point of 1625 \({}^{\circ}\)C--the density was needed to convert wt % to vol % during mixing of BT ceramic pastes.  
SEM images of the milled powder (Figure 2c) indicate that the particles had no spherical shape, but their aspect ratio was close to 1. While there were many particles of \(\approx\)1 \(\upmu\)m in size, plenty of them were smaller than one micrometer. They were attached to the larger particles or sited between them. The particles were not agglomerated, which improved the homogenization of the BT pastes.  
The estimation of particle size carried over with the image processing methods (Figure 2d) resulted in distribution with an average particle size of 267 nm. The use of the equivalent diameter measure for particle size analysis was rational since particles' aspect ratio was close to 1. The presence of multiple submicron-sized particles in the milled powder complicated polymerization of BT pastes due to the enhancing of the light scattering phenomena. Such pastes typically demonstrate decreased polymerization depths. Thus, the polymerization testing of BT pastes conducted in this study was done under unfavorable conditions in terms of light propagation into the bulk of feedstock material.
## 3 System Model and Problem Formulation
### System Model  
In our work, the following assumptions were made regarding the configuration and network of sensors: (1) the network consists of rotatable stationary sensors, mobile sensors, and a control center (such as a sink or base station); (2) the control center is responsible for collecting location information of sensors and broadcasting scheduling instructions to the sensors; (3) all sensors have the same parameters; (4) the position of each sensor can be obtained (GPS or positioning algorithm); and (5) the sensor is equipped with a built-in clock synchronization algorithm and pre-set routing protocol to ensure normal communication.  
We set the region of interest (ROI) of the IoT sensing layer as a regular two-dimensional rectangle. The size of the ROI is \(L\times H\). Two types of sensor are deployed in the ROI: stationary sensors and mobile sensors. Other parameters of the sensor are consistent, except for mobility. All sensors can be represented by the set \(N=\{n_{1},n_{2},n_{3},\ldots n_{n}\}\). Stationary sensors can be represented by the set \(S=\{s_{1},s_{2},s_{3},\ldots s_{n_{s}}\}\), where \(n_{s}\) represents the stationary sensor. The mobile sensors are represented by the set \(M=\{m_{1},m_{2},m_{3},\ldots m_{n_{m}}\}\), where \(n_{m}\) represents the mobile sensor.  
The directional sensors in a DSN have adjustable sensing directions and limited sensing angles, with the sensing range of a sector. The sensing radius and angle are important factors that constrain the sensing ability of directional sensors. The position and working direction can also affect the sensing area of the sensor. Its sensing model can be described using a 4-tuple:  
\[\left\langle L(n_{i}),R_{s},\delta,\beta_{t}(n_{i})\right\rangle \tag{1}\]  
where the position coordinates, sensing radius, and sensing offset angle (half of the vertex angle) of node \(n_{i}\) are \(L(n_{i})=(x_{n_{i}},y_{n_{i}})\), \(R_{S}\), and \(\delta\) respectively. \(\beta_{t}(n_{i})\) represents the working direction at time \(t\).  
The detection model of directional sensors uses a 0/1 model. When any point within the monitoring area meets the following two conditions, that point can be detected by the sensor :  
\[\sqrt{\left(x_{n_{i}}-x\right)^{2}+\left(y_{n_{i}}-y\right)^{2}}\leq R_{S} \tag{2}\]  
\[\delta\leq\arctan\left|\frac{y-y_{n_{i}}}{x-x_{n_{i}}}\right|\leq\delta+\beta_ {t}(n_{i}) \tag{3}\]
## 3 Results and Discussion  
### Volatile Component Analysis  
The volatile aroma substances of the wines were detected using HS-SPME-GC-MS. As shown in Table S1, 52 volatile substances were detected in the test samples. These substances included esters, higher alcohols, volatile fatty acids, C6 compounds, carbonyl compounds, terpenoids, and isoprenoids. The total aroma content measured for each wine sample ranged from 174.58 to 290.26 mg/L. Among the samples, wines made from Merlot 343 grapes had the highest total contents of aroma components. It is also significantly higher than the aroma content of Merlot wines from some areas of Ningxia and Hebei in China Ningxia and Hebei (2010). However, those made from the four Pinot Noir clones had relatively low total contents of aroma components, which were 60-76% of those in the Merlot 343 wines (Table S1).  
Depending on the source, volatile aroma substances can be classified into varietal and fermentation aromas. The varietal aroma originated from grape berries, including C6 compounds, terpenoids, and norisoprenoids. The fermentation aromas originated from the ferentation process, and the corresponding compounds included higher alcohols, esters, fatty acids, and carbonyl compounds [1]. As shown in Figure 1, the total varietal aroma contents of the wine samples from the four Pinot Noir clones were higher and significantly different from those of the wine samples obtained from the other grape varieties. This result is similar to that of Pinot Noir wines from the Central Otago region of New Zealand [16]. However, the wines produced from the Cabernet Franc clones contained significantly lower proportions of varietal components, which were 27.8-47.8% of those of the Pinot Noir wine samples. The Cabernet Franc wines from the Hebei region of China presented similar results in terms of varietal aromas [17]. The 16 wine samples differed slightly in fermentation aroma contents. In general, the wines from the Merlot and Pinot Noir clones exhibited higher and lower levels of total fermentation aroma content, respectively, and the Pinot Noir VCR 9 sample from the latter group had the lowest.
## 3 Results
### Performance of Feedback Control Scheme for Phase Fading  
The experimental system for measuring the performance of the feedback control scheme is shown in Figure 3. The outputs of the 3 PDs are digitized by a multichannel data acquisition card, after which the data are processed using the Labview software. The feedback signal is outputted by a digital analog converter in the data acquisition card. Note that only a reflector is needed here to reflect the light back into the self-designed probe, without the piezoelectric ceramic vibrator. The power of the laser source was adjusted to 4 mW, after which three steps were followed. First, we removed the reflector and observed the outputs of the three PDs. Different outputs will be observed due to more loss through the circulator in PD1 and the performance index difference of the \(3\times 3\) coupler and PDs. Second, we adjusted the outputs to be approximately equal by digitally correcting the signals, and we recorded the value. Third, we placed a reflector vertically in front of the self-designed probe and applied the feedback control algorithm to the interference system. The signal of \(P_{32}\) was recorded when the feedback control was carried out, as shown in Figure 6. It can be seen that the phase fading is  
Figure 5: The result of the degradation coefficient caused by the polarization.  
compensated wherever the initial phase difference is located. Figure 6a,b show the phase compensation results when the initial phase difference are located at \([-\pi/2,\pi/2)\) and \([\pi/2,3\pi/2)\) respectively. It is seen that the phase difference is compensated directly to the ideal point in the right direction; however, the positive and negative of the phase difference are the opposite of the increase and decrease of the phase compensation. About 65 ms are needed to compensate for the phase when the feedback control scheme is implemented, with the initial phase difference being far from the ideal point. The time is reduced at the moment when the phase difference is controlled close to the ideal point. Meanwhile, the performance of the feedback control scheme is measured under the condition that the power of the light reflected back into the self-designed probe is changed via the adjustment of the angle of the reflector. The results show that the proposed feedback control scheme can compensate for the phase difference without adjusting the parameters manually when the power of the light reflected back into the self-designed probe changes.
# Gap Analysis of Ambient Electromagnetic Noise Measurements Stored in the ITU Data Banks
## 3 Gap Analysis
### Measurements per Environment Type  
As the current empirical man-made noise model in P.372 differentiates between 4 environment types, the individual measurements in the Radio Noise Data Bank also contain labels describing the local environment type. Tables 3 and 4 provide an overview of the absolute and relative number of measurements per environment type per country.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Country** & **Nr of Meas.** & **Rel. nr. of Meas.** \\ \hline Germany & 654 & 79\% \\ Japan & 80 & 10\% \\ The Netherlands & 61 & 7\% \\ Spain & 1 & 0\% \\ UK & 33 & 4\% \\ USA & 4 & 0\% \\ \hline Total & 833 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of outdoor measurement locations per country. The dominant contribution is highlighted in yellow.  
Figure 6: (**a**) Geographical distribution of the man-made noise measurements in Germany. No measurements were made in Quiet Rural areas, as this exceeded the capability of the equipment. (**b**) Geographical distribution of the man-made noise measurements in the United Kingdom.  
Figure 7: (**a**) Geographical distribution of the man-made noise measurements in the Netherlands. (**b**) Geographical distribution of the man-made noise measurements in Spain (single location).  
In the Radio Noise Data Bank, the environment types are labeled according to the definitions given in Recommendation ITU-R SM.1753:  
\begin{tabular}{l l} \hline \hline REMR & Remote Rural (P.372: Quiet Rural) \\ \hline RURL & Rural \\ \hline RESD & Residential \\ \hline CITY & City \\ \hline URBN & Urban \\ \hline INDA & Industrial Area \\ \hline ROAD & Highway \\ \hline OFFC & Office area \\ \hline \hline \end{tabular}  
From Table 4 it becomes clear that not every country equally distributed their measurements over locations in the different environments. The dominant contribution for each environment is highlighted.
# Context-Aware System for Information Flow Management in Factories of the Future
## 3 Context-Aware Application
### CA-FoFS Architecture  
The \"Context Aware Factory of Future System\" (CA-FoFS) is based on a layered architecture, adopting a service/event-oriented approach, and employs context awareness to dynamically support factory users. This layered approach enables the provision of contextual information, integration with various data sources, and supports multiple applications for end users, accessible through various devices [33].  
The CA-FoFS architecture is depicted in Figure 1 and consists of five independent layers: the devices layer, applications layer, services layer, integration layer, and lastly, the data layer.  
The devices layer facilitates access to contextualized information for users, thus helping them to make decisions. This layer contains various devices that can access the applications in the _application layer_.  
The applications layer contains all the applications that can be used by users, and these applications use the services of the _service layer_ to be able to perform their information-providing functions.  
The services layer is responsible for providing the functions used by applications. These services can access data through the integration layer or the Context Engine.  
The Context Engine is the context-aware application proposed in this article. This application acts as an intermediary between the application services and the context data that is accessed from the _integration layer_. Its responsibility is to monitor changes in context and, if it identifies changes that are considered important, to generate alerts/notifications. In ad  
Figure 1: CA-FoFS architecture.  
dition, it is responsible for various functionalities for disseminating contextual information based on parameters such as users involved or locations in the factory, among others.  
The integration layer has the responsibility of providing an access point to the data available in the data layer, through requests for access to that data, and normalizing it into a common format.  
The data layer contains all the data sources from which services and applications can obtain a variety of information. This layer contains company data, stored in databases or other data repositories. This data covers a wide variety of information, including information on production lines, workers and so on, as discussed in Section 3.1.
## 3 Experimental Setup  
The experimental setup for the optical memory buffer is shown in Figure 2a. Multiple frequency carriers are generated using a microresonator [27]. The microresonator consists of a silicon nitride (Si\({}_{3}\)N\({}_{4}\)) waveguide with a cross-section of 1.5 \(\upmu\)m \(\times\) 0.9 \(\upmu\)m, and a ring radius of \(\sim\)119 \(\upmu\)m. The waveguide is pumped using an external cavity laser diode (ECDL) at 1559 nm light in the anomalous dispersion region, with coupled input power to the waveguide of 32.5 dBm. The resonator's corresponding quality (Q) factor and the cavity linewidth are \(1.3\times 10^{6}\) and 150 MHz, respectively. Figure 2b shows the spectrum of the generated soliton Kerr comb using the microresonator. The generated comb's free spectral range (FSR) is \(\sim\)192 GHz. A zoomed-in view of the comb frequency lines over the optical C-band (1527.7-1567.6 nm) is shown in Figure 2c, where the measured power of the carriers next to the pump is \(-\)10 dBm, and the OSNR values of the carriers ranged between 18-31 dB (at 0.05 nm optical spectrum analyzer resolution).  
The comb carriers are double-sideband modulated with the LFM signal using a communication-grade Mach-Zehnder modulator (MZM) (40+ Gb/s Intensity Modulator, Eospace, WA, USA), biased at the quadrature. The modulated carriers are then sent to FBG1 (LC-FBG) and FBG2 (CS-FBG). The 7 m functional section of FBG1 emulates continuous dispersion of approximately 2000 ps/nm over the C-band, whereas FBG2 performsits dispersion-compensating function for all the C-band channels belonging to the 50 GHz ITU grid. Both FBGs are off-the-shelf items, and the combined insertion loss is \(\sim\)7 dB. The optical signals are amplified in a specially ordered 22 dB gain erbium-doped fiber amplifier (EDFA) with 1.2 m effective length to minimize latency. Next, a tunable liquid crystal on silicon (LCoS) optical filter (WaveShaper 1000S, FINISAR, Sunnyvale, CA, USA) is used to select the wavelength channel of interest, corresponding to the desired delay of the tunable optical memory buffer. Finally, we convert the signal to the RF domain using a 40 GHz photodiode.  
Figure 2d shows the optical spectrum of the concatenation of the LC-FBG and the CS-FBG when we illuminate the concatenation of the FBGs with a broadband optical source. We observe the reflected 100 accessible optical channels having 50 GHz spacing. Since the CS-FBG is sampled chirping yields 100 channels, while the LC-FBG is chirped over a length of 7 m with a round trip-trip delay associated with it, it should be assumed that every channel will have \(\frac{7\times 2(\text{round trip})}{2\times 10^{8}}\cdot\frac{1}{100}\approx 0.7\) ns compared to its neighboring one.
# SDToW: A Slowloris Detecting Tool for WMNs
## 7 Conclusions  
This work presents a detection and blocking tool for the Slowloris attack on WMNs. We performed a behavior analysis of the referred attack to extract relevant information from the Web server traffic. We developed filters to collect traffic information. Those filters permitted the correct attack identification and subsequently blocked the ongoing attack.  
We used the WMN structure, creating different modules with predefined objectives. The modules work in different locations inside the network, executing different tasks. These modules reduce the resource consumption overhead and permit one to block the attacker using its physical address. In the experiment using six clients, our tool achieved 66.7% less processing consumption and 89.1% less memory consumption than Snort. SDToW blocks the attacker near its origin and thus avoids unnecessary traffic on the network.  
We conducted experiments to analyze the impacts of traffic list size and the number of hops. The results of these experiments provide a more precise understanding of the required time to block attacks. Therefore, the experiments reveal that SDToW can mitigate an ongoing Slowloris attack before the Web server's resource depletion. Moreover, by blocking the attacker on its first hop, SDToW limits the attacker from trying new attacks on different servers on the WMNs.  
Our solution does not limit the number of parallel connections per user. Hence, by avoiding this limitation, SDToW has a lower incidence of false positive errors. In further research, we intend to analyze the impact of DDoS on our solution and enhance our tool to provide efficient detection of distributed Slowloris attacks.  
Conceptualization, D.M.M.; data curation, V.d.S.F. and C.A.M.d.S.; formal analysis, J.A.G. and G.d.B.V.; investigation, V.d.S.F., J.A.G., C.A.M.d.S., G.d.B.V. and D.M.M.; project administration, D.M.M.; software, V.d.S.F., J.A.G., C.A.M.d.S., G.d.B.V. and D.M.M.; writing--original draft, J.A.G., G.d.B.V. and D.M.M.; writing--review and editing, C.A.M.d.S. and D.M.M. All authors have read and agreed to the published version of the manuscript.  
CEFET/RJ and CNPq.  
**Acknowledgments:** The authors would like to thank CNPq and CEFET/RJ for the financial support given to this research and development work.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Effectiveness of High-Intensity Therapy in Rehabilitation of Parkinson's Disease Patients
## 5 Discussion
### Motor Function and Functional Independence (Primary Outcome)  
A key finding of this study was the significant enhancement in motor performance across all high-intensity therapy groups. This supports the primary outcome related to motor function, as defined in Section 2.2. Exergame and Cycling therapies, in particular, yielded substantial improvements in walking speed (10 MWT), endurance (6 MWT), and Functional Independence Measure (FIM) scores. These improvements reflect more efficient neuromuscular coordination, higher gait adaptability, and greater independence in daily activities. These results confirm prior findings that high-intensity, movement-based interventions can facilitate motor learning and plasticity in patients with PD, thereby enhancingoverall functional mobility [16]. Agility therapy also demonstrated notable gains in movement control and coordination, affirming its relevance as a viable high-intensity strategy. Conversely, Robot-assisted (Cmill) training showed limited improvements in motor function, likely due to the constrained movement environment and reduced active engagement. These improvements were not only statistically significant but also exceeded established thresholds for clinical relevance. Specifically, gait speed improvements of \(\geq\)0.10 m/s are generally considered clinically meaningful in patients with PD, and our data showed average increases of -0.30-0.50 m/s. Furthermore, improvements in FIM scores of \(\geq\)22 points are indicative of clinically significant functional gains, with our intervention groups improving by \(\sim\)25-30 points on average. In addition, the changes observed in balance-related outcomes (e.g., Berg Balance Scale, Tinetti) exceeded the Minimum Detectable Change (MDC) values for individuals with PD. By distinguishing between statistical and clinical significance, our findings support the real-world impact of high-intensity rehabilitation on mobility and independence in [PERSON]'s disease.
# Long-Term Forecasting of Electrical Loads in Kuwait Using Prophet and Holt-Winters Models
## 3 Results and Discussion  
The Prophet and Holt-Winters models are implemented in R and Python, using real data of electrical daily peak load from Kuwaitip powerplants from 2010 to 2020. The two models' outputs are plotted in Figures 4 and 5, along with the real electrical peak of daily loads. The data include Kuwait's peak loads from 2010 to 2020. Moreover, the forecasted from 2020 to 2030, along with a 99% confidence interval, are included. The forecasted region of this period is enlarged and presented in Figure 6, along with the 99% confidence interval for both models.  
The graphs in the previous figures demonstrate that there has been a steady increase in the values of daily peak loads with a yearly seasonality for both models. The two models share several key features, such as trend similarity of the forecasted maximum and minimum peaks. However, the Holt-Winters model generally shows a higher magnitude of the forecasted maximum and minimum peaks than the Prophet model. The superiority of the models is assessed from different aspects by  
Figure 3: Rolling origin cross-validation process.  
three indicators: accuracy, generalization, and robustness. Also, the future peak loads forecasting for Kuwait is discussed, and the maximum capacity needed by 2030 is investigated.  
Figure 4: The measured peaks of daily loads data in Kuwait: the trained, predicted, and forecasted data using the Prophet model with a 99% confidence interval.  
Figure 5: The measured peaks of daily loads data in Kuwait: the trained, predicted, and forecasted data using the Holt–Winters model with a 99% confidence interval.
## 3 Results and Discussion
### Effect of Compressor Speed and Ambient Temperature on Optimal Gas Cooler Air Volume Flow Rate
#### 3.3.2 Influence of Ambient Temperature on Optimal Gas Cooler Air Volume Flow Rate  
Figure 10 shows the cooling capacity and the maximum COP with the rise in air flow rate under different ambient temperatures. The air temperature at the gas cooler inlet was 30/35/40 \({}^{\circ}\)C, while the air temperature at the evaporator inlet was kept at 27 \({}^{\circ}\)C, respectively. Furthermore, the compressor speed was set to 3500 rpm. It is important to highlight that at  
Figure 9: Influence of compressor speed on the optimal gas cooler air volume flow rate.  
the condition at the ambient temperature of 40 \({}^{\circ}\)C and the air volume flow rate of 500 m\({}^{3}\cdot\)h\({}^{-1}\), the discharge temperature and/or the discharge pressure were above the safety limits.  
As the air volume increased from 2500 m\({}^{3}\cdot\)h\({}^{-1}\) to 3000 m\({}^{3}\cdot\)h\({}^{-1}\), the COP value increased from 2.37 to 2.42 at the ambient temperature of 30 \({}^{\circ}\)C (i.e., with a variation rate of 2%). Furthermore, the COP value increased from 2.13 to 2.17 at the ambient temperature of 35 \({}^{\circ}\)C (i.e., with a variation rate of 1.8%), while the COP value increased from 1.75 to 1.78 at the ambient temperature of 40 \({}^{\circ}\)C (i.e., with a variation rate of 1.7%). Therefore, it could be seen that the optimal air flow rate was basically the same under different ambient temperatures, which was about 2500 m\({}^{3}\cdot\)h\({}^{-1}\).