esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Rolling Motion of a Ship at Zero Speed and Sailing in Regular Beam Waves in a Resistant Environment
#### 3.2.2 Second Case: Large Td  
In this case, when the Td of a ship is large, \(\
icefrac{{Tw}}{{Td}}\to 0\) and \(\beta\to 0\). Considering that \(\omega^{2}=\left(\
icefrac{{2\pi}}{{Td}}\right)^{2}\to 0\), Equation (8) can be expressed as follows:  
\[\tan\beta=\frac{2\lambda_{1}}{\text{Td}}\cdot\frac{1}{\left(-\omega_{\text{W} }^{2}\right)}=-\frac{2\lambda_{1}}{\text{Td}}\cdot\frac{\text{Tw}}{2\pi}=- \frac{\lambda_{1}}{\pi}\cdot\frac{\text{Tw}}{\text{Td}}, \tag{18}\]  
Then, \(\tan\beta\to 0\); \(\beta\to 0\); \(\cos\beta\to 1\).  
As in the first case, the effect of the induced rolling motion is analysed. Therefore, according to Equation (7), for the induced motion, the following equation is obtained:  
\[\theta_{\text{f}}=\frac{\theta_{\text{MW}}\cdot\sin\left[\frac{2\pi}{\text{Tw }}\cdot\mathfrak{t}\right]}{1-\frac{\text{Td}^{2}}{\text{Tw}^{2}}}, \tag{19}\]  
The denominator takes a high value, and consequently, the induced rolling motion is small. Moreover, according to the results shown in Table 2, corresponding to the first case (very small Td), it is observed that in the trough and the crest of the wave, the induced rolling motion is also zero in the present case. Furthermore, the maximum value of the induced rolling angle is also reached at the middle of the wave slope, although this value is smaller than in the first case. These conclusions can be verified in Figure 5, where the same sea state condition (\(\text{Tw}=10\) s) is simulated, but with a ship natural roll period of Td = 16 s, which can be found in passenger vessels. Therefore, considering the same sea state conditions, the maximum rolling angle for a'stiff' ship with Td = 6 s is 0.155 rad (Figure 4), while for a 'tender' ship with Td = 16 s, it is 0.063 rad (Figure 5).  
Figure 4: Induced rolling angles for \(\text{Tw}=10\) s and Td = 6 s.  
As in this case study Td is large, i.e., the metacentric height (GM) is small, the ship has a reduced righting moment. As a consequence, during the rolling motion, the masts of the ship tend to slowly follow the normal of the wave. The maximum angle of absolute rolling is small compared to the relative rolling motion with respect to the normal. This circumstance can lead to seawater washing over the deck easily.
## 1 Introduction  
In the coming years, optical wireless communication (OWC) will play an increasingly important role in the development of networks with high capacity and density. There are several advantages to using this technology over radio frequency networks. This technology provides high transmission speeds, requires less installation time, is license-free, secure, has low error rates, and requires little initial investment. Among its applications are truncking networks, connecting buildings, underwater communications, deep space communications, and satellite-to-ground communication [1]. Currently, ground-to-space and ground-to-aircraft communications rely on microwave technology. Eventually, aircraft-to-aircraft links will be OWC. Inter-aircraft optical wireless communication systems can transmit data at speeds of several Gbps over long distances of many kilometers. A satellite-to-ground communication system has been developed utilizing OWC technology [2].  
Even though OWC technology has many merits, it also has several disadvantages, including scintillation loss (being sensitive to temperature variations caused by the Earth's heat rise), geometric loss, the attenuation of beam-spreading power, absorption loss (photons absorbed by water molecules or CO\({}_{2}\)), atmospheric attenuation, and scattering loss [1]. In addition, the ground-underwater communication system can support the development of services like deep-sea mining, high-definition video transmission, and offshore exploration through underwater wireless optical communication (UWOC). It is, therefore, possible to generate high-speed as well as long-distance OWC transmission by using satellite-ground-underwater integrated systems [3].  
The mode division multiplexing (MDM) method can be utilized to upgrade the capacity of an OWC link that uses spatial light modes as the information carrier. The orbital angular momentum (OAM) mode has been widely used to improve the capacity of OWC links incorporating the spatial orthogonality of distinct OAM modes [4]. Due to its distinguishability as well as orthogonality through distinct limitless charge numbers, OAM acts as an auxiliary degree of freedom in support of de-/multiplexing and for enhancing overall system capacity [5]. To reduce the complexity of digital signal processing in MDM, the satellite-ground-underwater OWC system is utilized. In this case, the complexity is quantified by the equalizers that are used in MDM reception [6].  
Additionally, OWC technology can be deployed efficiently for satellite, ground, and underwater applications. With an OWC transmission scheme, satellite-ground and ground-underwater orbital links can reach long distances with optimum sensitivity to receive signals. An OAM beam offers an increased system capacity as well as spectral efficiency due to its intense level of information carrying capability. In space communications, OWCs are a suitable choice for achieving ultra information transport capacities [7].
# Collecting, Processing and Secondary Using Personal and (Pseudo)Anonymized Data in Smart Cities
## 5 Application of Anonymization Tools and Privacy-Preserving Techniques
### Privacy-Preserving Techniques
#### 5.2.3 Multiparty Computation  
One approach for vehicular MPC communication is described by [152] that makes a cooperative control strategy incorporating efficient MPC, reducing latency and integrating a secret function sharing scheme. The MPC can be performed using a separate map on different clusters, where each cluster has different vehicles, which together calculate the average energy demand of a given cluster with secure multiparty computing. One vehicle from each cluster is then chosen as the cluster leader which sends the computed result to the destination. To avoid identification of certain vehicles by the address, \"shuffling between the cluster leaders\" is also possible.  
There are other applications of multiparty computation, namely using two-party computation for a privacy-preserving location recommendation scheme for LBS [155]. The scheme supports multi-attribute queries and returns accurate results while ensuring privacy protection for both the service provider and the users. The proposed scheme is based on the Paillier cryptosystem [156] and uses secure equal test protocols to check the equality of encrypted values. The security of the scheme is analyzed in the semi-honest model and experimental results demonstrate its practicality in real-world applications, making it a potential solution for privacy-sensitive smart city applications.
# Summarization of Remaining Life Prediction Methods for Special Power Plants
## 3 Future Research Directions
### Dynamic Data-Driven RUL Prediction Method
#### 3.2.3 RUL Prediction Method under Multi-Working Conditions  
For the multi-working conditions of special power plants, the kurtosis and cliff degree characteristics can be extracted according to the measurement data in order to effectively identify each working condition, which can be transformed into the RUL prediction problem driven by small-sample and non-equilibrium dynamic data. The dynamic data obtained through the soft sensor method of dynamic parameters under each working condition can be analyzed according to the above steps. Finally, based on the similar trajectory method (STM), a dynamic data-driven multi-working condition RUL prediction model is constructed.
# Enhancing Clay Soil's Geotechnical Properties Utilizing Sintered Gypsum and Glass Powder
## 2 Experiment Program
### Materials  
#### 2.1.1 Clay Soil  
This investigation's clay soil came from a 2 m deep hole dug in the northern part of Nicosia, in the center region of Cyprus. The chosen site lies within a river basin characterized by alluvial deposits. Prior to utilization, the collected soil underwent a drying process at 105 \({}^{\circ}\)C. To evaluate the soil's physical characteristics, a series of laboratory tests were conducted, encompassing Atterberg limits [29], sieve analysis [30], and specific gravity measurements [31]. By the Unified Soil Classification System (USCS) [29], the tested soil was classified as non-organic clay with low plasticity (CL). Furthermore, the specific gravity  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multicolumn{1}{c}{**Properties**} & \multicolumn{1}{c}{**Clay**} & \
\begin{tabular}{c} **Sintered Gypsum** \\ **Powder** \\ \end{tabular} &
\begin{tabular}{c} **Glass** \\ **Powder** \\ \end{tabular} \\ \hline \multicolumn{4}{l}{**Consistency Limits (ASTM D4318-17e1) [29]**} \\ \multicolumn{4}{l}{Plasticity index (\%)} & 26 & - & - \\ \multicolumn{4}{l}{Liquid limit (\%)} & 46 & - & - \\ \multicolumn{4}{l}{**Specific gravity (ASTM D854-14) [30]**} & 2.66 & 2.33 & 2.64 \\ \multicolumn{4}{l}{**Particle-Size Distribution (ASTM D6913/D6913M-17 [31]**} \\ \multicolumn{4}{l}{D\({}_{50}\) (mm)} & 0.005 & 0.1 & 1 \\ \multicolumn{4}{l}{Clay (diameter \textless{} 0.002 mm) (\%)} & 40 & - & - \\ \multicolumn{4}{l}{Silt (0.002 mm \textless{} diameter \textless{} 0.075 mm) (\%)} & 54 & 25 & 2.53 \\ \multicolumn{4}{l}{Fine sand (0.075 mm \textless{} diameter \textless{} 0.425 mm) (\%)} & 6 & 75 & 25 \\ \multicolumn{4}{l}{**Standard Compaction Characteristics**} \\ \multicolumn{4}{l}{**(ASTM D698-12e2) [32]**} \\ \multicolumn{4}{l}{Maximum Dry Density (kg/m\({}^{3}\))} & 1700 & - & - \\ \multicolumn{4}{l}{Optimum moisture Content (\%)} & 17 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical characteristics of clay soil, sintered gypsum, and glass powder.  
was found to be 2.66. The grain size distribution of the studied sample is illustrated in Figure 1.
# Real-Time Fall Monitoring for Seniors via YOLO and Voice Interaction
## 6 Discussion and Conclusions
### Conclusions  
This study proposed and validated a real-time fall detection system that combines YOLOv11-based pose estimation with an on-device voice interaction module to improve detection accuracy and reduce false alarms. The system demonstrated high precision and recall across 200 simulated sequences, and its modular architecture enables local processing, privacy preservation, and quick emergency response. Despite promising results, we limited testing to non-elderly participants in controlled environments. Future work will focus on expanding real-world validations with elderly users, enhancing voice interaction with adaptive NLP models, and integrating wearable or ambient sensors to further increase robustness. These developments aim to support the deployment of intelligent, context-aware monitoring systems that empower aging-in-place with safety and dignity.  
Conceptualization, methodology, software, investigation, resources, formal analysis, and validation, E.T. ([PERSON]), A.-M.V., A.A. and E.T. ([PERSON]); data curation, E.T. ([PERSON]) and A.-M.V.; writing--original draft preparation, E.T. ([PERSON]), A.-M.V., A.A. and E.T. ([PERSON]); writing--review and editing, A.A., A.-M.V. and E.T. ([PERSON]); supervision, project administration, and funding acquisition, A.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Romanian Ministry of Research, Innovation, and Digitalization, grant number 13N/2023 (PN 23 38 05 01). The APC was funded by MDPI.  
The data presented in this study are openly available on Le2i dataset at [[https://www.kaggle.com/datasets/tuyenldvn/falldataset-invia](https://www.kaggle.com/datasets/tuyenldvn/falldataset-invia)]([https://www.kaggle.com/datasets/tuyenldvn/falldataset-invia](https://www.kaggle.com/datasets/tuyenldvn/falldataset-invia)) (accessed on 25 April 2025).  
The authors gratefully acknowledge the contribution of the Romanian Ministry of Research, Innovation, and Digitalization for the development of the NeuroPredict Platform inside the project \"Advanced Artificial Intelligence Techniques in Science and Applications\" (contract No. 13N/2023 (PN 23 38 05 01)) for the period 2023-2026.  
The authors declare no conflicts of interest.
# Performance Evaluation of the New Compound-Carrier-Modulated Signal for Future Navigation Signals
## 3 Evaluation Criteria
### Spectral Separation Coefficient  
Mutual interference and compatibility with GNSS signals sharing the same frequency band, are the fundamental factors for the frequency coordination [27]. The Spectral Separation Coefficients (SSCs) represent the conventional approach to indicate and evaluate the degradation caused by the interference signals at the correlator output [28]. Thus, the correlator output can be expressed as:  
\[P_{o}=\left|\left|\int_{-\infty}^{\infty}\left\{\sqrt{P_{i}}S_{i}\left(f\right) +\sqrt{P_{d}}S_{d}\left(f\right)\right\}H\left(f\right)df\right|\right|^{2} \tag{4}\]  
where, the \(P_{d}\) and \(P_{i}\) refer to the power level of the desired signal and the interfering signal respectively, \(S_{d}(f)\) and \(S_{i}(f)\) refer to the spectrum of the desired signal and the interfering signal, \(H(f)\) is an ideal matched filter, thus equals to the conjugate of the desired signal spectrum.  
Figure 5: Comparison of ACFs for NSCC baseline structures within 20 MHz.  
Assuming that the cross-spectrum term is zero due to the non-correlation between the desired signal and the interfering signal. Then, the Equation (4) can be simplified as:  
\[P_{o}=P_{i}\int_{-\infty}^{\infty}G_{i}\left(f\right)G_{d}\left(f\right)df+P_{d} \int_{-\infty}^{\infty}G_{d}^{2}\left(f\right)df \tag{5}\]  
where, the terms _Gs(f)_ and _Gif(f)_ stand for the normalized PSDs of the desired and interfering signals, respectively. The SSCs can thus be expressed as the following form:  
\[\kappa_{ss}=\int_{-\infty}^{\infty}G_{s}^{2}\left(f\right)df \tag{6}\]  
\[\kappa_{si}=\int_{-\infty}^{\infty}G_{i}\left(f\right)G_{s}\left(f\right)df \tag{7}\]  
Clearly, \(\kappa_{ss}\) and \(\kappa_{si}\) quantify a signal's self-imposed SSC and mutual interference SSC, respectively.
# Sustainable Additives for the Production of Hydrogen via Sodium Borohydride Hydrolysis
Additionally, additives can be introduced to optimize the hydrolysis of NaBH\({}_{4}\) by increasing the stability and improving hydrogen generation rates, leading to higher yields and better overall process performance. Several works have studied the effect of additives in NaBH\({}_{4}\) hydrolysis, including common chemicals and organic polymers [28; 29]. [PERSON] et al. [28] employed ammonia borane NH\({}_{3}\)BH\({}_{3}\) to prevent particle agglomeration, improving the dispersibility of NaBH\({}_{4}\) and making the resulting composite more porous. With this additive, 90% of hydrogen could be released at 70 \({}^{\circ}\)C after 1 h, with hydrogen yields of 9 wt% (considering reacted water). On the other hand, [PERSON] et al. [29] analyzed the effect of the addition of a cellulose derivative, observing a rise in the rate and yield of hydrogen generation by increasing the non-polar hydrogen-electrolyte interactions and, consequently, improving the affinity for H\({}_{2}\) storage in the liquid phase due to its solubility. Another interesting work highlights the use of metal nanoparticles supported on polyacrylamide (PAM), known for its highly water-absorbent character, in NaBH\({}_{4}\) methanolysis [30]. Furthermore, the use of NaOH-poly(acrylamide) hydrogels for the hydrolysis of NaBH\({}_{4}\) has been explored for H\({}_{2}\) generation [31]. Moreover, [PERSON] et al. [32] studied sodium dodecyl sulfate (SDS) as a surfactant and stabilizing agent to investigate its effect on the size, morphology, and catalytic activity for H\({}_{2}\) production using NaBH\({}_{4}\). Other authors, such as [PERSON] et al. [33], utilized \(\beta\)-cyclodextrinn (BCD) as a capping agent in the synthesis of nanoparticles. This approach was selected to prevent agglomeration and ensure stability. Nevertheless, the number of works dealing with additives in the hydrolysis of NaBH\({}_{4}\) remains scarce in the literature and, thus, this work aims to optimize the hydrolysis of sodium borohydride through the employment of four different additives that improve the rate of hydrogen generation. The selected additives were non-toxic, environmentally friendly, and had the ability to enhance the reaction efficiency and stability, including sodium carboxymethyl cellulose (CMC), polyacrylamide (PAM), sodium dodecyl sulfate (SCS), and \(\beta\)-cyclodextrintrin (BCD). The obtained results provide valuable insights into the storage and production of hydrogen through NaBH\({}_{4}\) hydrolysis in the presence of sustainable additives. The use of these additives improves the reproducibility and stability of the reaction, facilitating its control and scalability for industrial applications.
## 5 Experimental Results
### Experimental Results for BCC Algorithm  
In this section, we discuss modeling, results and the corresponding analysis of the proposed server consolidation method. We first estimate the power reduction from proposed BCC server consolidation algorithm for both wired and wireless networks. Next, we evaluate the network-level performance with the consolidation algorithm in an HPC data center with network-level simulations. Before presenting and analyzing the results we describe the data center traffic generation procedure and simulation platform in the next subsections.
# Highly Efficient GaN Doherty Power Amplifier for N78 Sub-6 GHz Band 5G Applications
## 3 Circuit Design of RF DPA
### Design of Harmonic Suppression Network  
DPA's post-harmonic suppression network aims to reduce the high-order harmonic content generated by the amplifier and thus increase the efficiency of our amplifier. This is achieved by designing the circuit such that the two active devices in the Doherty configuration, the main amplifier and the peaking amplifier, operate out of phase with each other. The design of a maximally flat (Binomial) low-pass filter, to be fabricated using microstrip lines, has a cutoff frequency of 4.5 GHz. By following the proposed filter specifications, it was determined that an order of 7 (N = 7) would suffice. Referencing [32], the prototype element values can be determined. The next step is to calculate the new filter component values from capacitors (\(C_{k}^{\prime}\)) and inductors (\(L_{k}^{\prime}\)) by means of Equations (11) and (12).  
\[L_{k}^{\prime}=\frac{R_{0}L_{k}}{\omega_{c}}, \tag{11}\]  
\[C_{k}^{\prime}=\frac{C_{k}}{R_{0}\omega_{c}}, \tag{12}\]  
where \(\omega_{c}\) is the cutoff frequency; \(L_{k}\) and \(C_{k}\) are the component values for the original prototype; and \(R_{0}\) is the nominal characteristic impedance equal to 50 \(\Omega\). To increase its reliability for use in high-frequency RF circuits, the lumped-element filter can be transformed into a system of distributed elements, such as open- or short-circuited transmission line stubs. This conversion can be achieved through the use of Richards' transformation, which transforms lumped elements into transmission line sections. The filter elements are then physically separated by applying Kuroda's identities to the transmission line sections [32].  
Figure 12a presents the electrical transmission line schematic design of the harmonic suppression network, which has been carefully developed based on the previously discussed steps. The final layout of the network, shown in Figure 12b, displays the physical width and length of the design, including the integration of 35.35-ohm impedance transformers at the beginning of the network. To enhance the performance of the network, an additional series microstrip line has been added to ensure a 50-ohm connection to the output combiner, ensuring an appropriate width for welding the output port with the load. To ensure a smooth transition from the electrical microstrip lines shown in Figure 12a to the physical lines in Figure 12b, and prevent any junction discontinuity, a T-junction model has been added. To ensure that the performance of the network was not impacted by the additions made to it, an improvement was made to the network values using the Optimization Cockpit tool in ADS. The result of this optimization can be seen in the frequency response of the electromagnetic simulation results for the proposed network, which is displayed in Figure 13.  
Figure 11: Stability analyses based on K-\(\Delta\) test and \(\mu\)-test.
## 4 Case Study
### LSTM-Based Wind Prediction
#### 4.2.2 Comparison with Grey Model Prediction  
The same experiment is conducted on Grey model, and the prediction results with 24 different prediction steps are depicted in Figure 10. We compare the performance of LSTM with Grey model by root mean square error (RMSE) and maximum error (ME). As depicted in Figure 11, LSTM prediction has smaller RMSEs in most cases of 24 prediction steps, and MEs of LSTM prediction are always smaller than those of Grey model. Besides, the trajectories of LSTM prediction are more stationary.  
Furthermore, the computing time of the LSTM model is approximately 50 times shorter than that of the Grey model (hundreds of milliseconds versus tens of seconds). Since the sophisticated process of deep learning on historical data is conducted offline and the neural network is pre-trained, the prediction of LSTM is as simple as invoking a function. On the contrary, every prediction of Grey model involves solving a number of complicated equations.  
To summarize, LSTM prediction has advantages over the Grey model in terms of accuracy, robustness and computational efficiency.  
Figure 9: Prediction results of LSTM.
## 4 System Model
### Two-Level MPC State-Space and Optimization Model
#### 4.4.2 Two-Level MPC Programming Model  
* Programming Model of MPC2  
The programming model of MPC2 is similar to that of single-level MPC Case 0; therefore, the objection and constraints can be represented by Equations (10)-(16).  
* Programming Model of MPC1  
MPC 1 addresses Cases 1-5. In these cases, the pumped hydro storage variable is a constant.  
* MPC1 Objective Function of the Model  
\[\min\sum_{t=0}^{N}\left(w,\left|p_{t}^{\text{\tiny{ps}}}-p_{t}^{\text{\tiny{ phn}}}\right|+w_{t}p_{t}^{\text{\tiny{uu}}}-w_{t}p_{t}^{\text{\tiny{wd}}}\right) \tag{26}\]
* MPC1 Constraints  
Equations (11)-(15) are also the constraints of MPC 1 because the prediction horizon is reduced significantly, _i.e._, it is smaller than that of the single-level MPC, as described in Table 2.  
The MPC1 constraints of Assumption 1 are presented as follows:  
\[\text{Case1}:p_{t=0}^{\text{\tiny{ps}}}=p_{t=1}^{\text{\tiny{ps}}}=p_{t=2}^{ \text{\tiny{ps}}}=p_{t=3}^{\text{\tiny{ps}}}=p_{t=4}^{\text{\tiny{ps}}}=p_{t \_{-}initial}^{\text{\tiny{ps}}} \tag{27}\]  
\[\text{Case2}:p_{t=0}^{\text{\tiny{ps}}}=p_{t=1}^{\text{\tiny{ps}}}=p_{t=2}^{ \text{\tiny{ps}}}=p_{t=3}^{\text{\tiny{ps}}}=p_{t_{-}initial}^{\text{\tiny{ps}}} \tag{28}\]  
\[\text{Case3}:p_{t=0}^{\text{\tiny{ps}}}=p_{t=1}^{\text{\tiny{ps}}}=p_{t=2}^{ \text{\tiny{ps}}}=p_{t_{-}initial}^{\text{\tiny{ps}}} \tag{29}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & **Case 0** & **Case 1** & **Case 2** & **Case 3** & **Case 4** & **Case 5** \\ \hline \(N\) & 23 & 4 & 3 & 2 & 1 & 0 \\ \(t\) & 0–23 & 0–4 & 0–3 & 0–2 & 0–1 & 0 \\ \(p_{t}^{\text{\tiny{ps}}}\) & variable & Constant & Constant & Constant & Constant & constant \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters for different cases of two-level model predictive control (MPC1).  
\[\text{Case}4:p_{t=0}^{\text{px}}=p_{t=1}^{\text{px}}=p_{t\_initial}^{\text{px}} \tag{30}\]  
\[\text{Case}5:p_{t=0}^{\text{px}}=p_{t\_initial}^{\text{px}} \tag{31}\]  
The meanings of the five constraints are the same as those in Equation (17).