esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Transformative Impact and Ethical Implications of AI in Healthcare
### Regulatory Challenges in AI Healthcare: Adapting to AI's Evolving Nature
#### 5.2.2 Evolving Regulatory Frameworks for AI in Healthcare: Safety, Ethics, and Accountability  
Adapting regulatory frameworks is essential for AI's safe and ethical integration in healthcare. Continuous monitoring and updating of AI systems post-deployment are critical to ensuring their ongoing safety and efficacy. This process should include regular assessments of AI systems' regulatory status based on real-world performance and impact. Transparent reporting and accountability frameworks are necessary for addressing regulatory challenges, which require clear documentation of AI decision-making processes, data sources, and methodologies. A collaborative approach that includes regulatory bodies, healthcare providers, AI developers, and patients is vital for developing flexible and responsive regulatory frameworks that can keep pace with technological advancements. A risk-based approach to regulation is also crucial, tailoring the scrutiny and regulatory requirements to the potential risks and impacts of specific AI applications, particularly those involved in critical healthcare decisions [2].  
Ethical considerations, such as fairness, non-discrimination, and respect for patient autonomy, must be woven into the regulatory frameworks governing AI in healthcare. This includes proactively addressing potential biases in AI models that could lead to disparities in healthcare outcomes. The dynamic nature of AI technologies and the regulatory frameworks that govern them underscores the need for regulatory systems to evolve to match AI advancements. This evolution is significant in assessing AI's safety and efficacy, where traditional methods for evaluating medical software may not suffice. Furthermore, the liability and accountability associated with AI-driven decisions in healthcare add complexity to the regulatory landscape. Data privacy and security concerns are amplified by AI's reliance on large datasets, making it imperative that regulatory frameworks are robust enough to protect patient information while fostering innovation. Adapting the regulatory frameworks for AI in healthcare is a multifaceted endeavor that involves continuous monitoring, collaborative and risk-based approaches, transparency, and ethical considerations,ensuring that AI applications in healthcare remain safe, effective, and aligned with societal values [2; 27].
# Spaceborne LiDAR Systems: Evolution, Capabilities, and Challenges
## 7 The Future of the Spaceborne LiDARs
### Future Concepts
#### 7.2.4 LiDAR as a CubeSat Playload  
Recent developments in miniaturization have made it possible to equip CubeSats with LiDAR systems, opening new opportunities for affordable and frequent 3D Earth observation. These small satellites offer a cost-effective alternative to large, traditional missions, while still providing valuable data for applications like terrain mapping, forest monitoring, and atmospheric research. For example, Ref. [178] describes the design and analysis of a CubeSat equipped with a LiDAR sensor for environmental monitoring. Ref. [179] explores various LiDAR concepts for space applications using CubeSats. It covers short-range proximity LiDARs for 3D imaging in satellite rendezvous and sample capture missions, and long-range configurations (up to 1000 km) for planetary mapping and altimetry.
## 3 System Model
### Problem Formulation
The transmission efficiency of TWDM-PON enabled fronthaul is defined as follow:  
\[\eta=\frac{S-\sum\limits_{j=1}^{S}\sigma_{j}}{S} \tag{3}\]  
As we know, the tidal effect is clear during the day in both the smart homes of office and residential. Uneven requirements among RRUs results in load imbalance in fronthaul links. We take the load balancing in the wavelength dimensioning into consideration in the problem formulation. We use mean squared error (MSE) (similar to [42]) to indicate the load balancing state of the TWDM-PON enabled fronthaul network:  
\[LB_{fronthaul}=\frac{\sum\limits_{j=1}^{S}\sigma_{j}\cdot\left( \begin{array}[ ]{c}\sum\limits_{i=1}^{N}r_{i}\cdot s_{i}\cdot\delta_{Wj},(O_{i},R_{i})\cdot \
u_{CPRI}\cdot\mu_{Ri}\\ -\left(\sum\limits_{i=1}^{N}r_{i}\cdot s_{i}\cdot\
u_{CPRI}\cdot\mu_{Ri} \right)/\sum\limits_{j=1}^{S}\sigma_{j}\end{array} \right)^{2}}{\sum\limits_{j= 1}^{S}\sigma_{j}} \tag{4}\]where Boolean variable \(\delta_{Wj},(O_{i},R_{i})\) indicates that whether wavelength \(W_{j}\) is distributed to ONU \(O_{i}\) (or RRU \(R_{i}\)) for signal forwarding. Specifically, \(\delta_{Wj},(O_{i},R_{i})=1\) if \(W_{j}\) is allocated to ONU \(O_{i}\) (or RRU\(R_{i}\)), and 0 otherwise. \((O_{i},R_{i})\) means that there is a corresponding ONU \(O_{i}\) is connected with RRU \(R_{i}\) serving the small cell. Moreover, parameter \(\
u_{CPRI}\) represents the link rate of CPRI. The variable \(\mu_{Ri}\) indicates the compression ratio of RRU \(R_{i}\) (or ONU \(O_{i}\)) on the basis of bandwidth requirement in the corresponding small cell. Our objective is to enhance the energy and transmission efficiency of the proposed TWDM-PON enabled reconfigurable fronthaul. Moreover, we also consider the load balancing between the optical fronthaul links. Three sub-objectives are given by Equations (1), (3) and (4), respectively. We use INLP approach to optimize the proposed three sub-objectives with the following constraints:  
\[s_{i}\in\{0,1\},\ r_{i}\in\{0,1\},\forall_{i}\in\{1,\ldots,N\} \tag{5}\]  
\[\sigma_{j}\in\{0,1\},\ \delta_{Wj},(O_{i},R_{i})\in\{0,1\},\forall_{i}\in\{1, \ldots,N\},\forall_{j}\in\{1,\ldots,S\} \tag{6}\]  
\[\sum_{i=1}^{N}s_{i}\cdot r_{i}\cdot\delta_{Wj},(O_{i},R_{i})\cdot\
u_{CPRI} \cdot\mu_{Ri}\leq C_{W},\forall_{j}\in\{1,\ldots,S\} \tag{7}\]  
\[\sum_{j=1}^{S}s_{i}\cdot r_{i}\cdot\delta_{Wj},(O_{i},R_{i})=0,\text{or}\ 1, \forall_{i}\in\{1,\ldots,N\} \tag{8}\]  
\[\sum_{j=1}^{S}\sum_{i=1}^{N}\delta_{Wj},(O_{i},R_{i})=\sum_{i=1}^{N}s_{i}\cdot r _{i}. \tag{9}\]  
Value constraints of variables \(s_{i}\), \(r_{i}\), \(\sigma_{i}\), and \(\delta_{Wj},(O_{i},R_{i})\) are shown in Equations (5) and (6). Equation (7) limits the maximum aggregated CPRI signal transmission rate served by an active wavelength \(W_{j}\). Equation (8) indicates that only one wavelength can be assigned to each active ONU \(O_{i}\) (or RRU \(R_{i}\)), and zero otherwise. Equation (9) ensures that the total number of active ONUs (RRUs) obtained by two different ways is the same with each other.
## 4 Discussion
The effect of dose gradients on the volume of the detectors is ignored in this work. In particular, in the analyzed experimental data, the small pixel size (2.5 \(\times\) 2.5 mm\({}^{2}\)) should minimize this effect. Nonetheless, the dose-gradient in each detector of an array can be inferred from the measurement of the detectors in its neighborhood, opening the door for the real-time correction of this effect if necessary. However, this was beyond the scope of this work, where we have focused on the correction for recombination. The latter effect is bound to be more important when performing measurements at large dose rates/doses per pulse. The method proposed in this work may improve the dosimetry of radiotherapy treatments with ionization chamber arrays. The potential is limited for air ionization chambers in conventional and FFF radiotherapy, because such detectors can be designed to present negligible recombination effects. This is not the case with LICs, where recombination losses in FFF radiotherapy can easily reach \(\sim\)3-5%. Therefore, the correction of that effect can allow to obtain better agreements in relative dosimetry measurements (the normalization of the dose distribution to the maximum/reference dose would perturb the low-dose regions and the penumbra of the dose distributions). In addition, this would allow to perform accurate absolute dosimetry comparisons, rather than relying on relative dosimetry. Where this method may offer the most potential is in the verification of UHDR treatments, as it would facilitate the use of ionization chamber arrays in a situation where extremely high doses per pulse would limit the application on these devices. There is a need to develop novel methodologies for the dosimetry of UHDR beams (for example, the AAPM has recently established a task force to deal with this issue). The application in such situations will still require more experimental and simulation studies, paying special attention to the pulse structure of those beams. Such detailed studies were beyond the scope of this work, which has been focused on presenting the method. Future studies of the application to UHDR deliveries will be carried out with specific experimental data.
# A Deep Learning-Based System for Automatic License Plate Recognition Using YOLOv12 and PaddleOCR
## 4 Detection and Classification Method
### Training YOLOv12 for Vehicle License Plate Detection
* **Flips (horizontal/vertical)**: the image and bounding boxes are flipped;
* **Scaling (random resizing)**: enlarging or shrinking objects;
* **Random Crop**: randomly cutting a part of the image;
* **Rotation**: small rotations, useful in scenarios with varied orientations;
* **Brightness/Contrast Adjustment**: changes in brightness and contrast;
* **Mosaic Augmentation**: combines four images into one (very effective);
* **MixUp**: combining two images by partial overlay;
* **CutOut**: masking random regions of the image, etc. The outer images, from which the arrows originate, represent the original input images. The images pointed to by the arrows are the augmented versions generated from those originals. Various data augmentation techniques were applied to create these variants, such as brightness adjustments (either increasing or decreasing brightness), horizontal flipping, zooming (in or out), and others. The central images are already augmented; however, they were not displayed alongside the original ones to keep the figure uncluttered.
# Chinese Neural Question Generation: Augmenting Knowledge into Multiple Neural Encoders
## 2 Related Work
### Answer-Focus Question Generation  
[PERSON] et al. [12] pioneered the first NQG model using an attention Seq2 Seq model [11], which inputs a sentence into an RNN-based encoder and produces a question about the sentence using a decoder. The attention mechanism is used to assist the decoder pay attention to the most relevant parts of the input sentence while producing a question. However, this base model does not encode the target answer, thus may not be able to learn which information to emphasize for question generation.  
Several NQG models have investigated encoding the target answer information by either treating the answer's position as an extra input feature [14; 16] or by encoding the answer with a separate RNN [28; 29]. The first type of methods encodes each input word vector with an extra answer feature indicating whether this word is within the answer span. [PERSON] et al. [16] used the BIO (denotes the beginning, inside and outside of an answer) tagging scheme to implement this feature, while [PERSON] and [PERSON] [30] directly adopted a binary indicator. [PERSON] et al. [31] argues that the context words closer to the answer also require more attention from the model. Thus, they encoded position embeddings into the computation of the attention distribution, where the position distance between the word and answer indicated the level of attention. The study results showed that the model reached an extra BLEU4 percentage of 0.89 on the SQuAD. However, adding the answer feature may tend to generate questions that include answer words [29]. To address this issue, [PERSON] et al. [27] proposed a separate RNN encoder for the answer such that the answer is replaced by a special token for passage encoding. Inspired by [PERSON] et al.'s work, in our proposed multi-encoder sequence-to-sequence question generation framework, the answer information was encoded separately, and we investigated the impact of the encoding schema on the accuracy of question types and overall quality of generated questions.
# Design Criteria for High-Gradient Radio-Frequency Linacs
## 4 Field Emission Currents  
Metal surfaces exposed to high electric fields emit electrons, so-called _field emission currents_. The field emission currents interacting with rf fields of accelerating structures are commonly referred to as _dark currents_. Dark currents are typically observed in operating high-gradient accelerating cavities, but, unlike rf breakdowns, they are relatively easy to measure since they are present during every rf pulse and change little from pulse to pulse. Also, dark currents are not as disruptive to the accelerating structure operation as rf breakdowns but produce unwanted beam loading and ionizing radiation, which could blind beam diagnostics.  
A theory of field emission was developed for DCs and is well understood for microscopically simple emitters [102]. Field emission is believed to be a trigger for DC breakdowns, so a significant effort was directed toward understanding the dark current as a possible precursor to vacuum rf breakdown in accelerating structures [67; 103; 104]. However, even though dark currents in accelerating structures are easily measured, a quantitative understanding of the dark current parameters and their possible connection to the breakdown trigger remain elusive.  
I note an empirical observation, which is remarkably reproducible for numerous high-gradient cavities. The dark current magnitude is a violent function of applied rf fields and is typically graphed on a semi-log so-called Fowler-Nordheim plot using \(I_{e}/E^{2.5}\) vs. \(1/E\), where \(I_{e}\) is the magnitude of the current and \(E\) is peak surface electric field. The slope of the dark current on a Fowler-Nordheim plot represents the field-enhancement factor \(\beta\). The physics of  
Figure 9: The breakdown rate for the cryo copper SW cavity cooled to 45 K together with the breakdown rate for soft Cu, hard Cu, and hard CuAg cavities of the same shape vs. accelerating gradient. The iris aperture for all the cavities is \(a/\lambda=0.105\). The cavities are fed by a shaped rf pulse with 150 ns flat top.  
\(\beta\) in practical cavities is not understood, despite a significant number of hypotheses. During the high-gradient operation of structures, we typically observe that peak macroscopic surface electric fields multiplied by the \(\beta\) are equal to about 1 volt per angstrom, or 10 GV/m. We note that this is true for conditioned cavities, where the breakdown rate is relatively constant. This empirical observation is of little practical value because it cannot be used to predict rf breakdown rates, unlike peak pulse surface heating or a modified Poynting vector.
# QoS-Guaranteed Radio Resource Management in LTE-A Co-Channel Networks with Dual Connectivity
## 4 Evaluation
### Simulation Results
#### 4.4.4 Evaluation for the E-ICIC of QD-ABS Scheme  
Scenario 4 compares the performance of the Q-learning-based dynamic ABS (QD-ABS) scheme with that of the static ABS scheme. For the static ABS scheme, the ABS ratio was set to 0.5 according to most of the literature. Recall that the QD-ABS scheme adopts the Q-learning mechanism to dynamically adjust the ABS ratio according to the feedback of the system. Two scenarios were simulated, we first simulated the case where more UEs were MUEs. Specifically, the UE population of Macro BS was three times more than that of pico BS. In the second case, all UEs were evenly distributed.  
Figure 12 presents that the system QoS satisfaction ratio of the QD-ABS scheme was 4.06% higher than that of the Static ABS scheme in the first scenario. In the second scenario where UEs were evenly distributed, the proposed QD-ABS scheme was still better than the static ABS scheme, as shown in Figure 13.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **MTS in DC Mode** & **PF in DC Mode** & **PF in SC Mode** \\ \hline Fairness index & 0.50623 & 0.64226 & 0.68495 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Fairness index of the Max–min Threshold Scheduler (MTS) scheme and the Proportional Fair Scheduler (PF) scheduling.  
Figure 11: Satisfaction ratio of the different scheduling schemes in scenario 3.
# BeiDou Short-Message Satellite Resource Allocation Algorithm Based on Deep Reinforcement Learning
## 3 Algorithm Design
### Regional Division Strategy
The total path transmission loss is:  
\[\widetilde{L}_{total}=\sum_{i=1}^{v}\sum_{j=1}^{n}\ell_{ij}I_{ij}^{A} \tag{28}\]  
where \(\ell_{ij}=1\) indicates that there is an intersatellite link between \(a_{i}^{r}\) and \(SAT_{j}\), and \(\ell_{ij}=0\) indicates that there is no intersatellite link between \(a_{i}^{r}\) and \(SAT_{j}\). Secondly, after completing regional division, each short-message satellite usually needs to serve multiple subregions; as shown in Figure 4, \(SAT_{1}\) serves subregions I, II, IX, and X. Unlike the previous model, task requests in a subregion can be responded to by multiple satellites, and there are significant differences in the number and density of terminals in different subregions. Furthermore, each short-message satellite needs to allocate its own short-message processing resources to the subregions it covers. By using the resource allocation model proposed above, an improved satellite resource allocation model suitable for regional division can be obtained. As shown in Figure 5, a snapshot is divided into \(n_{t}\) time slices, and each short-message satellite resource is allocated to a certain proportion of its covered subregion (the value of the subregion ratio is explained in detail in the next section). The color of the squares in Figure 5 corresponds to the colors of the subregions in Figure 4b, indicating how the resources are allocated in the current snapshot. Resources from different satellites obtained in the subregion are recorded as the set \(\mathbf{R_{a^{r}}}=\left\{R_{a^{r}}^{1},R_{a^{r}}^{2},\cdots,R_{a^{r}}^{n} \right\}\), \(a\in A^{r}\), where \(R_{a^{r}}^{n}\) represents the resources allocated by \(SAT_{n}\) to subregion \(a^{r}\), and there is \(\sum\limits_{a^{r}\in A^{r}}R_{a^{r}}^{n}=1\). Each subregion has a task queue, and its operation mode is similar to the resource allocation model mentioned in the second section. The difference lies in the task allocation in the task queue. In the improved model, based on the proportion of resources allocated by each satellite to the subregion, the tasks are divided among the covering satellites. For example, in subregion VII, the proportion of resources allocated by \(SAT_{1}\) accounts for 45% of the resources obtained by subregion VII, so then 45% of the tasks in subregion VII are allocated to \(SAT_{1}\). The unprocessed tasks in the current snapshot are copied to the next snapshot. Figure 5: Improved satellite resource allocation model adapted for regional division.
# Wake-Up Receiver with Equal-Gain Antenna Diversity +
## 2 Background
### Discussion on Diversity Systems  
Equal-gain systems can successfully increase the signal strength at their output only if the noise signals are uncorrelated and the data signals are correlated, as already pointed out by [20]. If there is a highly correlated noise signal present at the antennas, equal-gain-combining systems degrade in their performance and selection diversity can achieve a higher signal-to-noise ratio of the output signals then the other techniques. In addition, for practical reasons, selection diversity systems often measure the received signal strength indicator and not the signal-to-noise ratio, which also degrades their efficiency in high noise areas or in the case of co-channel interferences. To mitigate this effect, some selection diversity systems additionally measure the present bit error rate and if it falls below a certain threshold, they switch to another antenna, and it also has a lower received signal strength indicator [21].
# Non-Real-Time Wireless System for Lightning Effect Measurements
## 1 Introduction  
The proposed measuring system in this paper can be compared to a wireless oscilloscope while maintaining some of its functionality. It is designed for specific applications, in particular, measuring the value of discharges induced by surge generators. It consists of measuring probes and one receiver connected to a computer via a USB port. One receiver can manage 32 measurement probes using dedicated software. It is possible to choose many important parameters, e.g., number of samples, sampling frequency, release voltage, synchronization (e.g., common to all probes from one source). The prototype measurement system allows sampling with a resolution of 16 bit at 20 MSps, which gives a data stream equal to 320 Mbps. A very good representation of the measured signal value is provided by 16-bit resolution. The main criteria for determining the maximum sampling rate were the complexity of the probe, the cost of the components and long battery life (for measurements in places with no power supply). The main goal of the described project is to develop a low-cost measurement system for testing in places exposed to damage (to expensive recording devices). Overvoltage can damage equipment throughout the laboratory. It is possible to perform preliminary measurements while maintaining a high quality of recorded data without the risk of overvoltage. The use of such devices is justified among others during the study of lightning effects, both natural and simulated by specialized impulse generators. The wireless connection ensures separation from the hazard point, ensuring the safe operation of people during measurements. The currently used measurement systems are often based on copper cable connections (coaxial), which are apotential hazard [1]. Safety is guaranteed by fiber optic systems, which, in turn, are expensive. They have a short working time between charging and are more difficult to carry [2]. An alternative solution may be the use of the Power over Fiber technology, which allows the use of a single- or multi-fiber optical transmission path with the simultaneous supply of distributed measuring devices [3]. The limitations of such a system are its efficiency and energy saving [4]. The advantage of the proposed solution is the resignation from the physical connection of the measuring probe and the recorder. This makes it possible to quickly change the measuring point. There is no limit to the length of the cables or their insulation from the elements of the tested object through which overvoltage may occur.
# Comparison of L1 and L5 Bands GNSS Signals Acquisition
## 3 Assessment of Processing Time and Memory Requirements
### Summary  
To have a better overview of the previous expressions, Table 2 summarizes the processing time and the memory requirements for the different implementations.