esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Patient Population  
Out of an initially recruited 94 patients, 59 were ultimately enrolled in the study, while 35 were excluded due to either the lack of a follow-up PET-CT scan or incomplete clinical history. Clinical and demographic characteristics of the enrolled patients are summarized in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Characteristics** & **N (Percentage)** \\ \hline Patients & 59 \\ \hline Male sex & 51 (86.4\%) \\ \hline Median age y (interquartile range) & 86 (84–89) \\ \hline
**ECOG** & \\
0–1 & 47 \\ \(\geq\)2 & 12 \\ \hline
**Tumor Location** & \\ Head/neck & 52 (88.1\%) \\ Trunk & 4 (6.7\%) \\ Lower limbs & 3 (5\%) \\ \hline
**Tumor grade** & \\ G1–G2 & 38 (64.4\%) \\ G3 & 21 (35.6\%) \\ \hline
**Metastatic patients** & 8 (13.5\%) \\ Nodal metastases & 6 (10.1\%) \\ Distant metastases & 2 (3.3\%) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Patients’ clinical and demographic characteristics.  
The final cohort was predominantly male (86.4%), with a median age of 86 years. Regarding performance status, 47 patients were classified as ECOG 0-1 and 12 as ECOG \(\geq\) 2, indicating overall good functional capacity across the study population. The tumor was primarily located in the head and neck region (88.1%), with fewer cases arising in the trunk (6.7%) and lower limbs (5%). Histopathological analysis demonstrated that 64.4% of patients had low-grade tumors (G1-G2), while 35.6% exhibited high-grade (G3) disease. Metastatic involvement was observed in a minority of cases, with nodal metastases in 10% and distant metastases in 3.3% of patients. In terms of treatment, immunotherapy was used as a first-line approach in 77.9% of patients; additionally, 20.3% had undergone previous surgical intervention and 1.7% had received prior radiotherapy. Given the relatively homogeneous distribution of age (median 86 years, interquartile range: 84-89), ECOG (predominantly 0-1), and low metastatic burden in our cohort, we deliberately focused on baseline imaging features to isolate the independent predictive value of radiomics without introducing potential confounders.
## 5 Response of FOWT-SFFC Driven by Environmental Loads  
### Numerical Analysis by FAST and WAMIT  
The floating offshore wind turbine is mainly subjected to wave, wind and mechanical loads, which is a highly nonlinear dynamic system. For the high fidelity modeling and analysis of FOWT-SFFC, the coupled aero-hydro-servo-elastic time domain analysis method is adopted using FAST [44] and WAMIT [42]. FAST is NREL's primary computer-aided engineering (CAE) tool for simulating the coupled dynamic response of horizontal axis wind turbines. It synthesizes aerodynamics models, hydrodynamics models, control and electrical system (servo) dynamics models, and structural (elastic) dynamics models to enable coupled nonlinear aero-hydro-servo-elastic simulation in the time domain [47].  
The aerodynamic loads are calculated with the blade element momentum (BEM) theory, while the mooring loads are calculated using the quasi-static method. To calculate the hydrodynamic loads, the linear hydrostatic restoring forces, added mass and damping contributions from linear wave radiation, incident wave excitation from linear diffraction in regular or irregular seas and nonlinear viscous drag forces from incident wave kinematics and cage motions are considered. In the present study, a hybrid model is adopted, in which both potential-flow theory and Morison's equation are taken into account to calculate the fluid forces. The WAMIT output files of hydrostatic and hydrodynamic coefficients are called by FAST and the inverse fast Fourier transform is invoked to perform frequency-to-time-domain transforms. The nonlinear viscous drag forces are modeled through Morison's elements. The drag force on the copper alloy nets is ignored as the hydrodynamic forces on columns and pontoons account for almost all wave loads on fishing cage. However, to reflect the viscous damping of the copper alloy nets, an additional linear damping matrix is added into the input file of HydroDyn which is a time-domain hydrodynamics module embedded in FAST.
## 4 A Deep Learning-Based CSI Feedback for New Radio
### DFT-Based Preprocessing of CSI Information  
The first processing that is applied to the estimated channel matrix in the feedback task is the 2D discrete Fourier transform (DFT), with the goal of sparsifying the coefficients of \(\mathbf{H}\) in the angular-delay domain, as proposed in [12]. To deal with more antennas at the receiver, this operation is iterated \(M\) times, thus producing a different transformed spatial-frequency channel matrix \(\widetilde{\mathbf{H}}_{m}\) for each receiving antenna:  
\[\widetilde{\mathbf{H}}_{m}=\mathbf{F}_{d}\mathbf{H}_{m}\mathbf{F}_{a}^{H} \tag{6}\]  
where \(\mathbf{F}_{d}\) and \(\mathbf{F}_{a}\) are \(\widetilde{C}\times\widetilde{C}\) and \(N\times N\) DFT matrices, respectively. \(\mathbf{H}_{m}\) has size \(\widetilde{C}\times N\) and \(m\) is the index of the receiving antenna. Figure 3 shows an instance of \(\mathbf{H}_{m}\) with \(N=8\) and \(\widetilde{C}=600\).  
As the inter-arrival time between rays only spans a certain range, only a few elements of the matrix \(\widetilde{\mathbf{H}}_{m}\) contain significant information, while the vast majority of the matrix elements are made up of coefficients close to zero; thus, DFT processing is necessary. It is obvious that the remaining columns of \(\widetilde{\mathbf{H}}_{m}\) can be eliminated and that only the core \(C<\widetilde{C}\) columns are significant (Figure 4).  
A truncated version of \(\widetilde{\mathbf{H}}_{m}\) is then obtained as a first step to decrease the feedback overhead, denoted as \(\widetilde{\mathbf{H}}_{m}^{t}\) and having size \(  
the matrices \(\mathbf{H}_{\text{m}}^{\text{Csi-RS};}\); this means that the matrices relative to distinct receiving antennas are conveyed through the CNN on the same input channels.  
The reasons behind this approach are several:  
* we do not need to train a different CNN model for each distinct receiving antenna;
* the number of parameters learned by the CNN is not affected by the number of receiving antennas \(M\). This means that in massive MIMO scenarios, where \(M\gg 1\), the complexity of the model does not increase with \(M\) and, thus, the risk of running into overfitting phenomena remains limited;
* if the different receiving antennas were spatially correlated, this correlation could be exploited during the learning process and, probably, smaller training datasets would be sufficient to obtain effective and generalized models.
## 3 Materials and Methods
### Reliability Issues of RED/BLACK Architecture  
While the RED/BLACK bifurcation provides crucial security assurances, the isolated nature of the domains introduces inherent reliability challenges for such systems. Strict physical and logical separation creates confined silos, limiting collective redundancy strengths compared to integrated systems.  
Isolating the high-assurance RED environment incurs overheads, directly impacting service availability and mean time between failures compared to the relatively open BLACK domain. Obtaining like-for-like RED and BLACK redundancy entails proportionally high costs due to stringent RED assurance needs. Robust partitioning also restricts shared fate resilience offered through loosely coupled architectures.  
These reliability differentials must be carefully evaluated when considering innovations like the cross-domain resource sharing proposed in this paper. The architecture must balance reliability gains from added redundancy against the risks of improperly segregating RED and BLACK environments when resources are mixed. A reliability model mapping domain interdependencies could help assess these factors.
# Alkali-Activated Materials with Pre-Treated Municipal Solid Waste Incinerator Bottom Ash
###### Abstract  
This study presents the results of an experimental campaign on the use of municipal solid waste incinerator bottom ash (MIBA) and fly ash (FA) as precursors for the production of alkali-activated materials. MIBA was subjected to a pre-treatment stage in response to two issues: high metallic aluminum content, which reacts in a high pH solution, releasing hydrogen; and low amorphous content of silica-, aluminum- and calcium-bearing phases, which translates into a limited formation of reaction products. The proposed pre-treatment stage oxidizes most of the metallic aluminum fraction and compensates for the low reactivity of the material via the formation of additional reactants. Different combinations of MIBA and FA were tried--mass-based ratios of 0/100, 25/75, 50/50, 75/25, and 100/0 for MIBA/FA. Two mix designs of the alkaline activator with sodium hydroxide and sodium silicate were evaluated by varying the Na\({}_{2}\)O/binder and SiO\({}_{2}\)/Na\({}_{2}\)O ratios. These mortars were tested in the fresh and hardened state. The results showed that the pre-treatment stage was effective at stabilizing the dimensional variation of MIBA. Despite the lower reactivity of MIBA, mortars with 50/50 of MIBA/FA presented a maximum 28-day compressive strength of 25.2 MPa, higher than the 5.7 MPa of mortars made with MIBA only.  
alkali-activated materials; municipal solid waste incinerator bottom ash; fly ash; pre-treatment
## 3 Research of the Key Elements
### Calculation of Equilibrium between Fiber Optical Repeater and Cellular Phone
This can be expressed as Equations (15) and (16): \[\text{Time Delay eNB}\text{ is }T_{Delay\text{ }eNB}=3.3\text{ }\upmu\text{s}\times\text{L/2}\] (15) \[\text{Time Delay FR is }T_{Delay\text{ }FR}=5\text{ }\upmu\text{s}\times\text{L}+5\text{ }\upmu\text{s}+3.3\text{ }\upmu\text{s}\times\text{L/2}\] (16) where: L is the distance between eNB and FR. The first term represents the delay introduced by the fiber cable, the second term represents the delay introduced by the fiber optical repeater, and the third represents the RF propagation delay, as shown in Figure 8.  
The upper limit is instead fixed by the system. It is represented by the specialist that actually looks for the signals with different paths with the aim to re-combine. The specialist has a scan window of approximately 20 \(\upmu\)s.  
Therefore, Equation (16) minus Equation (15) should be satisfied as:  
\[P_{Delay\text{ }FR}=P_{Delay\text{ }eNB}<20\text{ }\upmu\text{s} \tag{17}\]  
Therefore, the maximum \(L\) is 3 km. Figure 8: The time delay in eNB to FR. Figure 9: Time delay from FR to FR. 2. Two signals have been retransmitted from two different fiber optic RUs, as shown in Figure 9. \[3.3\ \upmu\mathrm{s}\times L/2+5\ \upmu\mathrm{s}\times L-3.3\ \upmu\mathrm{s}\times L/2<20\ \upmu\mathrm{s}\] Therefore, the maximum distance S is 4 km. Attention should be paid to the downlink budget of fiber optical repeaters. In the case of the fiber optical repeaters in practice, once the implementation of gain adjustment has been considered, isolating the fiber optical repeater and the antenna prevents local oscillations and localization in the fiber optical repeater.
## 3 Proposed Method
### Hardware Improvement  
The hardware implementation of this paper is basically the same as that of [29], which mainly includes comparators, multiplexers and adders. The difference is that tree-cascaded method is used to implement multi-input multiplexers with the nodes of 2-input and 3-input multiplexers in this paper. In [29], the implementation method of multi-input multiplexers is not mentioned, only that it is based on 2-input multiplexer. The libraries used in this paper all contain 2-input and 3-input multiplexers, and in general a 3-input multiplexer has roughly the same area as two 2-input multiplexers, but the timing is better. To reduce the critical path, carry-lookahead adders are used to replace normal adders. In order to make a better comparison, the same functions and parameters as in [29] are adopted for experiment and analysis. We consider 2\({}^{\mathrm{x}}\) as a typical function to analyze the advantages of the proposed method in this paper. Firstly, we set the following parameters: \(\mathrm{iw}=27\), \(\mathrm{qw}=14\), \(\mathrm{kw}=2\), \(\mathrm{MAE}_{\mathrm{def}}=1.29\,\times\,10^{-3}\), cycle_num = 7 and the range of independent variables is [0,1). The results of OML-PLAC are shown in Table 4. The number of segments and the final MAE are 18 and \(1.25\,\times\,10^{-3}\), while the two data in ML-PLAC are 24 and \(1.29\,\times\,10^{-3}\), respectively. The performance gains are due to optimized segmentation method and EA, and the hardware architecture is shown in Figure 5.  
Figure 4: The flow diagram of the EA method. Seventeen parallel comparators are used to determine the location of the input data. According to the results of comparison, the y-intercept and shifted data are selected for addition. Three multiplexers for data selection are parallel too, which are implemented by tree-cascaded 2-input and 3-input multiplexers. The cascaded circuit in the green box of Figure 5 is the muti-input multiplexer implementation circuit, and the red dashed line
###### Abstract  
To enable accurate evaluation of satellite laser communication terminals under solar outage interference, this paper presents the design and implementation of a solar radiation simulation system targeting the 1540-1560 nm communication band. The system reconstructs co-propagating interference conditions through standardized and continuously tunable output, based on high irradiance and spectral uniformity. A compound beam homogenization structure--combining a multimode fiber and an apodizator--achieves 85.8% far-field uniformity over a 200 mm aperture. A power-spectrum co-optimization strategy is introduced for filter design, achieving a spectral matching degree of 78%. The system supports a tunable output from 2.5 to 130 mW with a 50\(\times\) dynamic range and maintains power control accuracy within \(\pm\)0.9%. To suppress internal background interference, a _BRDF_-based optical scattering model is established to trace primary and secondary stray light paths. Simulation results show that by maintaining the surface roughness of key mirrors below 2 nm and incorporating a U-shaped reflective light trap, stray light levels can be reduced to 5.13 \(\times\) 10\({}^{-12}\) W, ensuring stable detection of a 10\({}^{-10}\) W signal at a 10:1 signal-to-background ratio. Experimental validation confirms that the system can faithfully reproduce solar outage conditions within a \(\pm\)3\({}^{\circ}\) field of view, achieving consistent performance in spectrum shaping, irradiance uniformity, and background suppression.  
The proposed platform provides a standardized and practical testbed for ground-based anti-interference assessment of optical communication terminals.  
Sun outage; stray light; solar radiation; optical trap; spectrum match; irradiance uniformity
## 3 Hybrid Model Based on CAE and CNN  
### Method for the Production of an Object-Oriented Remote Sensing Data Set  
To avoid the \"salt and pepper phenomenon\" caused by traditional pixel-based classification methods, an object composed of multiple pixels is used as the basic classification unit and then classified--this is object-oriented remote sensing classification.  
Object-oriented remote sensing classification technology compiles the set of adjacent pixels into objects to identify geographic entities of interest, making full use of the shape, texture, and spectral information of high-resolution remote sensing images. This occurs through two processes: object construction and object classification [10]. In this paper, the natural geographic boundaries of an object are obtained by performing multi-resolution segmentation of remote sensing images using the fractal net evolution approach, each pixel of an object maintaining its original spectral value, that is to say, an object has multiple distinct spectral values (as shown in Table 1). Then, the objects composed of multiple distinct spectral values are directly inputted into the proposed model and subsequently classified. This approach avoids the issue of treating the multiple distinct spectral values of an object as a single statistical value (as shown in Table 1) which cannot accurately express the spectral features of each pixel of an object in traditional object-oriented classification methods.  
CNNs require a constant dimensionality, but objects with natural geographic semantics obtained using multiresolution segmentation have a variety of sizes and are irregular in shape. Therefore, it is necessary to further process objects acquired using multiresolution segmentation, expand all of the objects into an \(n\times n\) size matrix, and set the pixel value of the expanded area to zero in order to highlight the boundaries of objects in images whose minimum value is greater than zero. An example is shown in Figure 1 below.
## 3 Architecture
### Data-Preprocessing Component  
Figure 4 shows the data-preprocessing component of MIRD. The primary input for this component is the Parquet archive of PQ data at a 1-h resolution, stored by the Data Ingestion module. Even though this Parquet archive contains the 31 variables delivered by PQM, this stage receives only the eight variables required to forecast PQ indexes. JSON files store the preprocessing parameters related to each PQM; those parameters are necessary to execute this component. The preprocessing component is executed daily and returns the time series containing preprocessed data for the eight required variables on the corresponding date. This data is joined with the results of the previous six daily executions to form the set of the last seven days; forecasting uses that information. A historical archive stores all previously preprocessed data sets. Scripts in this pipeline component are coded with the machine-learning library SciKit Learn.
## 4 Applications for IoT Technology in Smart Buildings
### IoT-Based Solar Power Monitoring  
IoT simplifies solar power panels' energy control and maintenance while retaining a low cost and high accuracy ratio. This IoT-based system for monitoring the solar power panels will focus on dust reduction in the panel to increase the power generation for practical usage [103]. The control of the energy generated and the cooling is refined dependent on the surface temperature by an Arduino-based detecting, controlling, and inciting framework. The IoT-based STS (Solar Panel Tracking System) can be incorporated and maintained. Depending on the new technology, a backtracking algorithm for e-tracking solar panels can be determined, and the implementation can be completed by employing IoT. The Raspberry Pi and flask framework are used to monitor the solar monitoring. Renewable energy use is shown in real-time through intelligent monitoring [104]. This aids the user in determining the amount of energy used. Impacts on renewable energy use and electrical challenges are examined.
## 4 Conclusions  
This article publishes the results of a study of LDPC-coded UWA communications, with m-MIMO and SC-FDE transmission technique, for four different receiver types: ZF, MRC, EGC, and IB-DFE. It was viewed that the IB-DFE tends to achieve the best overall performance, while the level of computational demand is highly reduced as compared to ZF, which also translates into less battery consumption. It was also viewed that a channel correlation of 0.3 or 0.5 between adjacent antenna elements could still be accepted because their performance degradation, as compared with the scenario without channel correlation, is moderate. Regardless of such performance degradation, this configuration is more realistic because the UWA communications consider carrier frequencies of the order of 15 kHz, whose wavelengths are too high. It is known that a minimum distance of around 3 to 4 wavelengths is required between adjacent antenna elements to ensure uncorrelated signals, which is difficult to achieve in such a UWA scenario. It was also viewed that increasing the correlation between adjacent antenna elements to 0.7 makes the system almost unacceptable, as it heavily degrades the performance.  
It was shown that increasing the number of receiving antennas of the MIMO system leads to a more accurate estimation and cancelation of the residual interference of the iterative receivers (MRC and IB-DFE), improving their performances closer to the MFB. This occurs because this article considers spatial multiplexing MIMO (multi-layer transmission), where the number of receiving antennas corresponds to the diversity order.  
Finally, it can be concluded that for the UWA environment, a system composed of LDPC codes associated with m-MIMO, using SC-FDE signals, and with the low complexity reached with the MRC receiver, makes such a composed system a good combination to achieve future evolutions of UWA communications, even in the presence of impulsive noise.  
Conceptualization, M.M.d.S., R.D., J.A. and L.M.L.O. All authors have read and agreed to the published version of the manuscript.  
This work is funded by FCT/MCTES through national funds and when applicable co-funded EU funds under the project UIDB/EEA/50008/2020.  
Not applicable.  
Data Availability StatementData was obtained through Monte Carlo Simulations, using Matlab, implemented by the authors.  
We acknowledge the support of FCT/MCTES, as described above in Funding. We also acknowledge the support of Autonoma TechLab for providing an interesting environment to carry out this research.  
The authors declare no conflict of interest.
# Dual-IoTID: A Session-Based Dual IoT Device Identification Model
## 3 Proposed Method
### System Overview and Methodology
#### 3.2 Identification of IoT Devices Using Dual-IoTID  
On the basis of the described workflow, we propose a dual-layer model for IoT device identification. The operational process can be outlined as follows: Given a session, the initial step involves extracting its network communication features, denoted as \(f\)1. Next, based on the word set established in Section 3.1.3, we transform the payload of this session into a word vector representation, termed _word_vec_. Following this, we feed _word_vec_ as the input into Classifier 1, which produces a confidence vector, _prob_vec_. Finally, we combine _prob_vec_ and \(f\)1 as the input for Classifier 2 to finally obtain the classification result T. The pseudocode of the method is shown in Algorithm 3.