esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
The observed deviations in the beam profiles from a Gaussian beam may come from the coupling of the THz antenna and the Si lens [43; 44; 45]. It has been previously shown that small differences in the position of the lens with respect to the antenna can have significant effects on the profiles of the emitted radiation [43; 44]. However, the antenna is positioned at the centre of the lens with an automated pick and place tool and the deviation from the central position is smaller than 20 \(\upmu\)m. Hence, the effect of incorrect positioning of the chip on the lens should be of minor importance. In addition, numerical models of the beam profiles, which will be discussed in detail elsewhere, show that the emitted beam profile is expected to have a circular symmetry when the lens is perfectly centred on the emitter. However, when a small off-axis displacement (<100 \(\upmu\)m) is present in the alignment of the lens with the centre of the emitter, both the main peak and side lobes can become asymmetrical and the main peak may become skewed. [PERSON] et al. [45] showed  
Figure 10: The mean and standard deviation of 5 beam profile scans at 600 GHz in the H-plane orientation. The error bars are shown in pink.  
Figure 9: Comparison of beam profiles at 600 GHz measured at various positions on a standing wave. (**a**) E-plane orientation where 35.6 mm and 35.9 mm are at the wave maximum. (b) H-plane orientation where 35.5 mm and 35.75 mm are at the wave maximum.  
that such a displacement can cause the peak of the radiation profile to be shifted from the centre by >10\({}^{\circ}\). This type of behaviour is particularly apparent in the high frequency profiles in both orientations, as seen in Figure 4.  
Internal reflections and refractions in the lens can give rise to side lobes in the beam. Although the dimensions of the silicon lens, especially the height of the hyper-hemisphere, are designed for minimal internal reflections, fabrication tolerances, which are as high as \(\pm\) 50 \(\upmu\)m, may influence the observed beam profile.  
Furthermore, the features in the beam profile differ significantly between the E-plane and the H-plane, with the E-plane having more regular profiles. At lower frequencies, the features appear more pronounced. This effect is attributed to the design of the antenna structure and the feeding point geometry, which connects the diode with the bowtie antenna (see Figure 1c). The influence of the feeding point and the antenna geometry on the beam profile of PIN diode emitters is currently under further investigation.
## 6 Positioning Analysis
### RTKLIB PPK Results
The MADs of the AV28 and ANN-MB antennas were lowered  
Figure 6: RTKLIB PPK MADs of the Up component of the kinematic positions before and after input data correction. The abbreviations (c) and (r) in the antenna names correspond to the utilized mounting brackets (circular plane and rectangular bracket). significantly by 31% (3.9 mm) and 23% (4 mm), respectively. This is particularly promising since it brings them to an almost comparable level to the more expensive antennas. The two cases with a rectangular adaptor showed the highest deviations but also the highest improvements after calibration, from 6.8 and 6.5 mm MAD down to 5.2 mm. Though the performance is not equal to the corrected results with a circular ground plane, they are on the same level as the uncorrected circular ground plane results and demonstrate the potential to significantly lower the multipath effects by using the proposed method. The observed effects are presumably caused by the reflections in the near field from the metallic rectangular adaptor. The corrections with 5\({}^{\circ}\) binning size perform slightly worse than the higher-resolution ones. However, only minor differences are visible between the 0.1\({}^{\circ}\) and 1\({}^{\circ}\) binning size results. It implies that the highest resolution of 0.1\({}^{\circ}\) is not necessary and that the 1\({}^{\circ}\) binning size appears to be a good trade-off between additional computing time requirements and smoothing potential outliers in the observations. Investigating the estimated PCOs from the RTKLIB processing using \(L_{1}+L_{2}\) data reveals consistency, even when no PCV is applied. This is quite understandable as our PCVs are computed from residuals from another RTKLIB processing, and the residuals are with respect to the \(L_{1}+L_{2}\) position estimate using only a calibration for the base antenna. The median PCO height estimations using \(L_{1}+L_{2}\) observations, before and after correcting for the PCVs, are illustrated in Figure 13.
# Programming Real-Time Sound in Python
## 3 Code Speedup
### Cython  
Cython extends Python by allowing explicit type declarations and direct compilation of C code [9]. Almost all of the Python libraries for scientific computing use Cython to minimize the computational burden, also because most algorithms provided by these libraries were already optimized on pre-existing pieces of C, C++ or even Fortran code. In fact, Cython is able to wrap functions written in different compiled languages. Unlike Numba, Cython is an ahead-of-time (AOT) compiler: during the so-called _cythonization_, C code is generated from additional (.pyx) files written by the developer during refactoring. Such files contain Python code (that is optimized automatically) and/or function wrappers of external routines in other languages. The efficiency of the cythonization can be measured as usual through code profiling: by just adding an option to the cythonization command, Cython even offers pictorial highlighting of the instructions proportionally to their computational cost.  
Listing A5 repeats the refactoring example seen in Listing A4, this time in Cython. Before the functions declaration, the global variable is defined static using the annotation _cdef_. Although Cython reduces the processing time by about 50% the interpretation of the same procedure, Numba generates a faster code at the cost of the initial optimization.  
On the other hand, when compared to Numba, Cython abstracts less layers, hence allowing easier debugging and optimization of the code. However, the creation of additional files (at least one) is needed. As an example, Listing A6 revisits the simple low-pass filter implementation seen in Section 2. After cythonization, which makes available the function _lp_ by compiling _low_pass.pyx_, the resulting _low_pass_ module can be launched from a Python program:  
* **c_low_pass.c** contains the procedure within the C function _lp_.
* **low_pass.pyx** binds the C file to the corresponding Python functions. From it, Cython compiles the corresponding module _low_pass_.
* **test_lp.py** calls the _low_pass_ module functions.  
For the sake of comparison, the Cython and Numba refactored code of the _OscSine()_ object class have been uploaded in GitHub, together with time profiling.
## 3 Proposed Algorithm and Formulation
### Proposed Algorithm
#### 3.2.2 Phase 2: Solving Distance and Saving Matrix  
The second phase of the algorithm is based on the Saving Matrix calculation, in which the distances between the depot and the customer locations are defined. At this point, it is important to underline that the case considered is based on a single depot, but equations could be extended to multi-depot problems. The Saving Matrix is calculated using the Saving Algorithm heuristic. The Saving Algorithm is a well-known heuristic for solving CVRP [17]. In the Saving Algorithm, distance and travel time savings are calculated when two routes merge together. Based on the saving order, routes are merged together sequentially and a better, more feasible solution will be generated [18].  
As Figure 2 shows, the basic idea of this algorithm is to combine routes together based on the saving matrix.  
Figure 2: Flowchart of Saving Algorithm.  
Let us give a complete graph \(G=(V,E)\), where \(E\) is the set of routes and \(V=\{v_{0},v_{1},\ldots,v_{n}\}\) the set of nodes, specifically (Figure 3):  
* \(v_{0}\) is the depot
* \(v_{1}\) and \(v_{2}\) are nodes
* \(\mathit{op}_{n}\) are performed service operations  
In the initial step, all nodes are connected to the depot (\(v_{0}\)) and N routes are generated as an initial solution (Phase 2.1). As shown in Figure 4, two different routes and policies can be generated and calculated through the Haversine formulation. It determines the great-circle distance between two points on a sphere given their longitudes and latitudes:  
* Policy \(a\): the vehicle starts from the depot, carries out the operation in \(v_{1}\) and then returns to the depot, heads to \(v_{2}\), carries out the operation and returns to the depot. The cost of this route is: \[C_{a}=2\overline{v_{0}v_{1}}+2\overline{v_{0}v_{2}}\] (11)
* Policy \(b\): the vehicle starts from the depot, carries out the operation in \(v_{1}\) and heads on to \(v_{2}\), carries out the operation and returns to the depot. The cost of this route is: \[C_{b}=\overline{v_{0}v_{1}}+\overline{v_{1}v_{2}}+\overline{v_{2}v_{0}}\] (12) Policy \(b\) allows a saving of: \[S_{b}=\ C_{a}-C_{b}=\ 2\overline{v_{0}v_{1}}+2\overline{v_{0}v_{2}}-\ \overline{v_{0}v_{1}}-\overline{v_{1}v_{2}}-\overline{v_{2}v_{0}}\] (13) We further assume that: \[\overline{v_{0}v_{2}}\ =\overline{v_{2}v_{0}}\] (14) \[S_{b}=\ C_{a}-C_{b}=\ \overline{v_{0}v_{1}}+\overline{v_{0}v_{2}}- \overline{v_{1}v_{2}}\] (15)  
Therefore, we can calculate the distances between all nodes of \(V\) and the savings related to them (Phase 2.2). The saving matrix is generated by calculating the saving distance and therefore the travel time for merging any two possible routes (\(i,j\)) together.  
Figure 4: Example of routes between nodes.  
Figure 3: Example of nodes.
# A Comprehensive Study on EDFA Characteristics: Temperature Impact
## 2 Analytical Model
### Propagation Equation  
The propagation equations are studied here in order to represent the full distribution of each signal while propagating through the fiber. Based on the [PERSON] and Desurvire model [2; 33] and by using the modified rate equations, the propagation equations are [14; 23]:  
\[\frac{\mathrm{d}\mathrm{P}_{\mathrm{P}}}{\mathrm{d}z}=\ \mathrm{P}_{\mathrm{P}} \mathrm{\Gamma}_{\mathrm{p}}\left(\mathrm{\beta}\sigma_{\mathrm{p}}^{\mathrm{e }}\mathrm{N}_{2}-\ \mathrm{o}_{\mathrm{p}}^{\mathrm{a}}\mathrm{N}_{1}\right)-\ \mathrm{\alpha}_{ \mathrm{p}}\mathrm{P}_{\mathrm{p}} \tag{9a}\] \[\frac{\mathrm{d}\mathrm{P}_{\mathrm{s}}}{\mathrm{d}z}=\ \mathrm{P}_{ \mathrm{s}}\mathrm{\Gamma}_{\mathrm{s}}(\sigma_{\mathrm{s}}^{\mathrm{e}} \mathrm{N}_{2}-\ \mathrm{\sigma}_{\mathrm{s}}^{\mathrm{a}}\mathrm{N}_{1})-\ \mathrm{\alpha}_{ \mathrm{s}}\mathrm{P}_{\mathrm{s}}\] (9b) \[\frac{\mathrm{d}\mathrm{P}_{\mathrm{ASE}}^{\pm}}{\mathrm{d}z}=\ \pm\mathrm{P}_{ \mathrm{ASE}}^{\pm}\mathrm{\Gamma}_{\mathrm{s}}(\sigma_{\mathrm{s}}^{\mathrm{ e}}\mathrm{N}_{2}-\mathrm{\sigma}_{\mathrm{s}}^{\mathrm{a}}\mathrm{N}_{1})\pm 2 \mathrm{\sigma}_{\mathrm{s}}^{\mathrm{e}}\mathrm{N}_{2}\mathrm{\Gamma}_{ \mathrm{s}}\mathrm{P}_{\mathrm{ASE}}^{\pm}\mathrm{\Delta}\mathrm{v}\mp\ \mathrm{\alpha}_{ \mathrm{s}}\mathrm{P}_{\mathrm{ASE}}^{\pm} \tag{9c}\]  
where z is the propagation direction through the fiber, and \(\mathrm{\Delta}\mathrm{v}\) is the homogeneous bandwidth.\(\mathrm{\alpha}_{\mathrm{s}}\) and \(\mathrm{\alpha}_{\mathrm{p}}\) are the fiber loss for the signal and pump, respectively. Both losses can be neglected in the case of short fiber, which is our case [2]. Here, \(\mathrm{\beta}\) is defined as the Boltzmann factor. It is the only factor, in addition to the emission and absorption cross-sections, that depends on temperature. It can be obtained from the Boltzmann distribution law [32]:  
\[\mathrm{\beta}=\exp\left(-\frac{\mathrm{\varepsilon}\mathrm{E}_{2}}{\mathrm{ k}_{\mathrm{B}}\mathrm{T}}\right) \tag{10}\]  
where \(\mathrm{\Delta}\mathrm{E}_{2}\) (\(\mathrm{E}_{2+}-\ \mathrm{E}_{2-}\)) represents the difference in energy between the upper and the lower sublevels' energies \(\mathrm{E}_{2+}\) and \(\mathrm{E}_{2-}\), respectively [3].
## 4 Results and Discussion
### Network Architecture
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Artificial Lighting, Original** & **Artificial Lighting, Matched** & **Natural Lighting, Original** & **Natural Lighting, Matched** & **Mixed Lighting, Original** & **Mixed Lighting, Matched** \\ \hline Slope & 0.9799 & 0.9961 & 0.9706 & 0.9452 & 0.9784 & 0.9825 \\ \hline Intercept & \(-\)0.0036 & \(-\)0.2091 & 1.1888 & 0.6051 & 0.5832 & 0.4086 \\ \hline
**RMSE/\(\mathrm{\SIUnitSymbolMicro g}\)**-mL**\({}^{-1}\) & **1.5769** & **0.7525** & **2.2900** & **1.9542** & **1.4828** & **1.3510** \\ \hline
**R\({}^{2}\) score** & **0.9979** & **0.9999** & **0.9953** & **0.9956** & **0.9966** & **0.9968** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluation of DL regression models with the best results for six different variations of testing. Figure 11: Training and validation losses for calculations of artificial lightning conditions for both original and (**i**) and histogram-matched (**ii**) images. The values of the numerical model evaluation and adjustment factors are presented in Table 4. The R\({}^{2}\) score was very high (above 0.99) in all cases; however, in every instance, the R\({}^{2}\) score was higher when standardization was performed. As for root mean squared error values, they were lower for matched images compared to the original ones. Depending on the lighting conditions, the learning rate changed from an initial value of \(10^{-3}\) to \(10^{-5}\).
# The Legal Framework for Space Debris Remediation as a Tool for Sustainability in Outer Space
## 2 Why Space Debris Remediation? The Facts
### The Status of the Outer Space Environment
Furthermore, even if up to 10 large objects are removed from low-Earth orbit per year, the debris growth in LEO is still likely to evolve negatively in the next 200 years [1]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Company** & **No. Satellites** & **Orbit** & **Mass/kg** & **Frequencies** & **Remarks** \\ \hline OneWeb & 900 & 1200 km, 18 polar planes & 175–200 & K\({}_{\mathrm{u}}\)-Band & First to register the frequencies at the Federal Communications Commission (FCC) \\ \hline Starlink & 4425 & 1100–1325 km, 83 planes, 53–81\({}^{\mathrm{n}}\) incl. & 100–500 & K\({}_{\mathrm{u}}\)/K\({}_{\mathrm{a}}\) & First 2 demo satellites in orbit (launched 22 February 2018); optical inter-satellite link \\ \hline Boeing & 2956 & 1200 km & — & V-Band & — \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Selection of recently proposed large satellite constellations; based on [12]. Long-term reference scenarios conducted recently within the H2020-ReDSHIFT project used a space debris population from LEO to GEO and a projection time frame of 200 years. Assuming 2-3 self-induced in-orbit explosions over the next 15 years, a post-mission disposal success rate of 60% (on 25-year orbits in LEO and to graveyard orbits in GEO) and collision avoidance against all objects in LEO, the results show that remediation of two objects per year decreases 12% of the final population [24]. Thus, it is expected that a combination of mitigation and remediation measures is needed to overcome the negative trends which will, with time, evolve into a catastrophic state if no effective action is undertaken. While an established (voluntary) framework for non-binding mitigation measures and some state practice exists through the adoption of specific measures for space debris mitigation in the national space laws of some states [25], the legal implementation of space debris remediation (SDR) is still in the making. The reasons for the slow pace of this development are, on the one hand, of a technological nature and, on the other, are due to the complex legal problems posed by SDR. In the following sub-section, an overview of the legal framework and the main challenges for establishing rules on SDR will be given.
# A Simple Relationship to Estimate Parameters of the Optimum Compaction Point
## 4 Discussion  
To complete the evaluation of the proposed equations to estimate \(w_{Opt}\) and \(\gamma_{dmax}\), other data taken from the literature published by [30] were used. These data were not used to build the proposed mathematical equations (Equations (2)-(4)).  
For these data, \(w_{L}\) is between 28 and 74%, \(w_{p}\) is between 12 and 39%, \(I_{p}\) varies between 12 and 46%, \(w_{opt}\) ranges between 13 and 40% and \(\gamma_{dmax}\) varies between 11.3 and 18.8 \(\mathrm{kN/m^{3}}\) (see Table 4).  
In addition, the estimated parameters by the proposed equations were compared to those estimated by [30] where Equation (5) and (6) were used. It is important to note that these data (Table 4) were used to build the Equations (5) and (6). Consequently, the correlation between measured and estimated values will be higher.  
\[w_{Opt}=0.94*w_{P} \tag{5}\]  
\[\gamma_{dmax}=0.22*(96.32-wp) \tag{6}\]  
Comparison results are presented in Figures 5 and 6, respectively, for \(\gamma_{dmax}\) and \(w_{Opt}\).  
These figures show that the values of r obtained from using the proposed equations by the author (0.998 and 0.984, respectively, for \(\gamma_{dmax}\) and \(w_{Opt}\)) are similar to those obtained from the equation proposed by [30] (0.999 and 0.989, respectively, for \(\gamma_{dmax}\) and \(w_{Opt}\)).  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & \(w_{L}\) **(\%)** & \(w_{P}\) **(\%)** & \(I_{P}\) **(\%)** & \(w_{Opt}\) **(\%)** & \(\gamma_{dmax}\) **(kN/m3)** \\ \hline _Minimum_ & 28 & 12 & 12 & 13 & 11.3 \\ _Maximum_ & 74 & 39 & 46 & 40 & 18.8 \\ _Mean value_ & 46.4 & 22.0 & 24.4 & 20.6 & 16.3 \\ \hline \end{tabular}
\end{table}
Table 4: Statistical parameters of soils taken from [30].  
Figure 4: Correlation between measured and estimated \(w_{opt}\) using estimated \(\gamma_{dmax}\) with data from [19].  
These results confirm the ability of the proposed equations to estimate adequately the compaction optimum point parameters for different soils, where _lp_ is between 7% and 71%.
# MIDOM--A DICOM-Based Medical Image Communication System
## 3 Midom
### MIDOM Components  
A simple usage of the MIDOM in a medical institution can be observed in Figure 4. Four main components can be easily distinguished, namely:  
* **MIDOM server--**The main component which facilitates the collaboration of medical specialists on sets of diagnostic procedure results, i.e., studies. This component hosts studies and related multimedia data, mainly in high-resolution images, and keeps track of the statuses of each study and consultation request. It also provides methods for system administration and performance monitoring, and performs computationally and storage-intensive operations in order to adjust to communication and processing power characteristics of the medical specialist and devices transparently to the system users;
* **PACS server--**This component, originally not an integrated part of MIDOM, is used for storage purposes, for efficient medical data accessing and fetching. By attaching PACS component with MIDOM, fast access to a large number of medical images and their import for usage was enabled;
* **Study Provider--**A component used by medical professionals seeking consultations with other colleagues. It allows inspection, selection, and extraction of diagnostic  
Figure 3: MIDOM in use.  
imaging data from the PACS server to form a study. It also allows for requests for consultations and the collection of feedback returned by medical specialists. The study provider component can manage the study and associated consultation requests' lifecycle. The main goals of this work have been developed through this component by enabling the study provider to access the PACS server and allowing it to create a study using image data from that server;
* **Medical Provider--**This component is usually implemented as a mobile part of the system. A medical specialist offers consultation services to the study provider in a standard flow. The study provider assigns consultation requests to a medical specialist then they seek further consultations about a particular study. A single study usually consists of medical images paired with their description and further explanation of the problem. A medical specialist can accept or reject a consultation request. If the consultation request is accepted, a medical specialist can inspect image data from the study for which the consultation has been requested and provide either textual or audio feedback. Medical specialists' end devices can be connected through various types of networks, which can cause significant delays in terms of latency or time needed to pass studies through network channels. This aspect is thoroughly addressed in MIDOM by appropriate tooling and required logic for compressing exchanged imaging data to optimize user experience, throughput, and data communication costs associated with mobile networks.
# The Role of AI-Based Chatbots in Public Health Emergencies: A Narrative Review
## 7 Challenges and Limitations of AI Chatbots in Infectious Disease Outbreaks
### Dissemination of Outdated Information  
During health crises, AI chatbots that rely on outdated data risk providing incorrect guidance, leading to public confusion and non-compliance with current health measures [112]. AI models, especially LLMs, often require continuous updates to reflect the latest health guidelines, yet maintaining this real-time accuracy is technically challenging and resource-intensive. While dynamic knowledge graphs and incremental learning techniques show promise for keeping AI systems updated [113; 114], gaps remain in ensuring the real-time integration of new data during fast-changing situations like pandemics.  
Technical measures, such as integrating real-time data from authoritative sources via APIs, are necessary to ensure the accuracy of chatbot responses [115]. A modular AI design allows for more flexible updates by separating the knowledge base from the language model, which can significantly reduce the need for full retraining [116]. Incremental learning techniques can also enhance the system's adaptability, enabling it to learn from new information without large-scale retraining [117].  
Socio-technical interventions, such as partnerships with health authorities like the WHO and CDC, ensure that AI systems receive timely updates [118]. User feedback systems allow individuals to report outdated information, helping improve chatbot accuracy and accountability [119]. Additionally, notifying users when significant updates occur fosters transparency and trust [120].  
Key research areas include improving real-time update integration and maintaining data consistency in AI models [121]. Understanding how outdated information affects user behavior and trust is essential for developing design strategies that promote engagement with accurate, up-to-date data [122].
# A Novel Adaptive Image Data Hiding and Encryption Scheme Using Constructive Image Abstraction
## 3 Experimental Results and Analysis
### Analysis of Security  
This section presents a performance evaluation of our image encryption algorithm, which is designed to encrypt the stego image, \(I_{S}\), and generate an encrypted image, \(I_{E}\). We adopt the security metrics proposed by [PERSON] et al. [14], which include visual perception, histogram analysis, correlation analysis, entropy, image sensitivity, and key security. Additionally, we conduct noise addition analysis to evaluate the security of our proposed scheme.