esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Thematic Review and Analysis
### Heat Source  
The impact of low return temperatures on heat sources varies depending on the type of heat source and the overall system design. Research consistently shows that a lower return temperature, combined with reduced flow rates, generally benefits performance near the heat source. However, the relationship between return temperature and heat generation efficiency can differ significantly across various types of heat generation units and their specific connection configurations. Therefore, it is crucial to examine return temperature issues from the perspective of the heat source. This section reviews the relevant literature, including studies on residential heating systems, to provide a comprehensive understanding of how different heat sources respond to low return temperatures.  
[PERSON] [23] identifies several limitations imposed by DH supply and return temperatures on heat production units. For gas condensing boilers, achieving condensation requires a return temperature below 55 \({}^{\circ}\)C, with optimal condensation occurring at around 35 \({}^{\circ}\)C. Higher return temperatures can prevent condensation and potentially cause corrosion. In contrast, biomass boilers have a minimum return temperature of 60 \({}^{\circ}\)C to prevent corrosion caused by condensation. Cogeneration units face issues due to large temperature differences between supply and return and low flow rates, which can result in asymmetrical cooling. Heat pumps, on the other hand, exhibit reduced efficiency and output at higher supply temperatures. [PERSON]'s research suggests that these constraints should be considered in the design of the primary loop interacting with the heat production unit, while secondary loops serving the DH network can benefit from reduced return temperatures [23].
# Dual-Probe Near-Field Phaseless Antenna Measurement System on Board a UAV
## 4 Discussion  
From the results shown in Figure 12, it can be noticed that the main beam is wider in the case of the on-site NF measurements. The most likely reason is that the reflections on the ground (see Figure 5), due to the realistic environment where the AUT is placed, make the measured NF data different from that of the ideal case of the anechoic environment in chamber measurements, thus resulting in the filling of the sidelobes adjacent to the main beam. It must be remarked that, in particular, the filling of the sidelobe at \(\theta=25^{\circ}\) can be observed using the measurements taken both with the previous and current prototype. This result justifies the importance of the on-site measurements, as they allow assessing the influence of the environment surrounding the AUT.  
Figure 12: Far field pattern. **(a)** From complex NF measurements at spherical range in anechoic chamber; **(b)** From amplitude-only information from on-site NF measurements, current prototype; **(c)** From amplitude-only information from on-site NF measurements, previous prototype [12]; **(d)** Comparison between NF-FF transformation from measurements at spherical range in anechoic chamber and on-site measurements.  
Concerning the quantitative assessment of the AUT radiation pattern, Table 1 shows a comparison of the radiation pattern parameters, namely \(-3\) dB beamwidth and directivity. In the case of using NF amplitude-only data, the UAV-based system for the antenna measurement presented in this contribution provides similar results as the NF measurements at the spherical range in the anechoic chamber. Furthermore, the improvements in positioning and geo-referring, as well as a better characterization of the dual-channel receiver, result in a better estimation of the radiation pattern with respect to the proof-of-concept shown in [12], as can be concluded from Table 1 and Figure 12.  
Another important result from this contribution is the capability of the pSRM for measuring off-centered high-directive antennas, providing diagnostics and radiation characterization. In previous works, such as [30], the validations for this kind of antennas were based on the simulations.
# A Decision Support System for Crop Recommendation Using Machine Learning Classification Algorithms
## 3 Materials and Methods
### Explainable AI  
Explainable artificial intelligence (XAI) is used in the role of validating the crop classification algorithms. It allows us to identify the important features in crop data, which influence decisions. It authenticates the performance of predictions by reducing the influence of noisy features. So, the model prediction shall be fair and unbiased in different demographic groups.  
Overall, XAI validates the classification methods by providing insights into decision-making, identifying features, and promoting transparency and error analysis.
## 3 Research Methodology
### Study Objectives and Research Questions  
With the rapid advancement of AI and DL, GANs have emerged as powerful tools for generating realistic data across various domains. As the complexity of GAN architectures grows, researchers have been increasingly interested in applying NAS to automate and optimize the design of GANs, allowing AI practitioners to focus on higher-level problems rather than manually crafting network architectures, which can be time-consuming and suboptimal [2]. Thus, developing effective NAS methods for GANs could significantly enhance the quality and efficiency of generative models.  
The main objectives of this study are to review and analyze the state-of-the-art NAS approaches for GANs, their techniques, and evaluation metrics. We conduct a comprehensive survey of the current landscape, focusing on the main types of NAS-GAN and identifying the important criterions of each type, such as underlying search space, techniques, search objectives, and evaluation metrics. Moreover, this study aims to identify gaps in the existing literature and highlight promising future directions to further advance research in this area.  
For the purposes of this study, we developed comparison frameworks and applied them to a set of prominent studies. The research questions that this review aims to answer are as follows:  
* RQ1: What NAS approaches are applied to GANs in the literature?
* To address this question, we identified the approaches present in the literature, highlighting both their benefits and limitations. By systematically reviewing existing methods, we were able to provide a comprehensive analysis that underscores the strengths of each approach while also acknowledging potential drawbacks and areas for improvement.
* RQ2: What are the key search spaces explored in NAS-GAN?
* To address this research question, we examined the search spaces utilized in the studied approaches, exploring their applications and additional relevant aspects. This investigation provided insights into how different search spaces are leveraged within the context of the approaches, highlighting their effectiveness and areas for potential enhancement.
* RQ3: What evaluation methods are used to assess the found architecture?
* To address this research question, we identified the metrics employed to evaluate NAS-GAN approaches and assessed their applicability. This analysis provided a detailed overview of the evaluation criteria used in the studies, examining their effectiveness in measuring the performance and suitability of the NAS-GAN approaches in various contexts.
* RQ4: What are the gaps in the research on NAS in GANs?To address this research question, we studied and analyzed the relevant literature, providing a comprehensive review of existing studies and identifying areas for future research. This approach allowed us to suggest potential directions for future work based on the gaps and limitations identified in the current body of knowledge.
# The Firmware Design and Implementation Scheme for C Form-Factor Pluggable Optical Transceiver +
## 3 CFP Firmware Design Scheme
### MCU Design
#### 3.3.1. NVM Function  
The CFP has NVM (or nonvolatile register(NVR)) that consists of areas such as \"CFP NVR\", \"VENDOR NVR\", and \"USER NVR\". A part of the flash memory of the MCU is allocated for the NVR memory, and the value is stored. When the CFP is powered on and initialized, the MCU implements the flash memory area value to be uploaded to the CFP register of the FPGA. The additional \"VENDOR PRIVATE NVR\" area is a space that can be used arbitrarily by the CFP manufacturer according to the design. In the proposed firmware design technique, the \"VENDOR PRIVATE\" space is used to store settings for CFP optical component performance optimization, correction values for DDM accuracy, and nonvolatile values required for other program operations
# Cartesian Constrained Stochastic Trajectory Optimization for Motion Planning
## 4 Results
### Cartesian Constraints Verification  
The first experiment validated tolerance keeping, with the goal being to ensure that any waypoint of a resulting trajectory did not exceed the maximal allowed translation distance and rotation angle. The translation tolerance was set to 1.0 m and rotation tolerance to 0.2 rad. The parameter stddev in the Cartesian Normal Distribution Sampling was (0.5, 0.5, 0.1, 0.1, 0.1), so that the amplitude of translation noise was 0.5 m on each axis and the rotation amplitude was 0.1 rad around each axis. This configuration has rather disproportional sampling amplitude compared to the tolerance as it is able to generate a configuration outside the desired tolerance in a single step. This was selected to ensure that the constraint worked even under such harsh conditions.  
The experiment dataset consisted of 3000 different resulting trajectories between the same start and the same goal state, which were the output from cartesian constrained STOMP. Figure 7 shows the differences between waypoints of an ideal cartesian linear trajectory and waypoints of resulting trajectories. The histogram in Figure 7a shows translation distance and the histogram in Figure 7b shows rotation angle, which represents the rotation difference between these waypoints. The histograms and results satisfied our expectation because no waypoint of resulting trajectories exceeded a translation tolerance of 1.0 m and rotation tolerance of 0.2 rad. Nevertheless, noise amplitudes were relatively high and tolerances were low, with all resulting trajectories valid.
# Primary User Traffic Pattern Based Opportunistic Spectrum Handoff in Cognitive Radio Networks
## 3 PUTPOSH Model
### Timing Diagram of the CRU Transmission in the Handoff Scheme  
Figure 4 delineates the timing diagram of a CRU transmission in the presented adaptive hybrid handoff scheme. CRU starts its transmission on channel 1 and after some intervals of time, the CRU proactively senses the arrival of the PU. The CRU compares the predicted PU waiting time and also the handoff delay on the same channel. Based on the comparison decision, the CRU reacts to perform a handoff on channel 2. During transmission on channel 2, the CRU senses the PU arrival and again makes the similarities of the overall service time (in case of stay and wait on the recent channel and performing the handoff).  
Moreover, CRU predicts the stopover of PU for a short duration and may wait and stay until PU completes its data transmission. As soon as PU resumes the channel, the CRU restarts its transmission on channel 2. After some time intervals, CRU again detects the PU arrival for a longer duration at channel 2 and performs the handoff at channel 1, another free channel. In the last part, the CRU performs a handoff to channel 3 to complete its transmission and may resumes the channel again.  
Figure 3: Queuing behavior of cognitive radio user (CRUs) and PUs (two channels and two queues).
## 2 Materials and Methods
### Methods
#### 2.3.6 Buffer Creation and Accuracy Assessment  
The 0.81 m airborne image subset by field was used to detect the cotton root rot-infested areas for each field. The results for all cotton fields were combined to a cotton root rot reference classification map. To accommodate the temporal variation and potential expansion of the disease from year to year, a 3-10 m buffer was usually added around the infested areas to become part of the treatment areas when prescription maps were created from fine-resolution airborne imagery [4]. In this study, a 10 m buffer (same as the pixel size of Sentinel-2 imagery) was added around the infested areas based on the airborne classification result to generate the reference prescription map for the study area.  
The BLR models constructed by the optimal spectral indices, texture features, and their combinations were applied to the Sentinel-2 images on the pixel level to detect the cotton root rot-infested areas for the eight fields and generate the classification maps. Similarly, a 10 m buffer was added around the infested areas to generate the prescription maps.  
In order to evaluate the feasibility of different Sentinel-2A features for identifying cotton root rot and creating prescription maps, the classification maps and prescription maps of three BLR modelswere exported to ArcGIS 10.2, which were then intersected with the classification map and prescription map of the airborne images, respectively. For quantitative accuracy assessment of the classification results, error matrices were used to determine the performance, OA, PA, and UA [50; 51].
## 1 Introduction
### Content and Contribution of the Study  
One of the most important motivations of the proposed study is to develop a user-centric and user-friendly tool for consumers to facilitate their participation process to the DR events, and hence to exploit the DR potential for residential end-users. With this objective, the interface has been developed so that it only requires smart phones to operate. Considering the fact that the number of smart phone users worldwide is over two billion today, using these ubiquitous devices for DR programs can be probably pointed out as the most effective way compared to the interfaces presented in the previous literature studies and real-world applications which use computers, tablets, smart screens and specific commercial devices for end-user interactions.  
None of the studies mentioned above or not referred to here considered dual sides of the DR application, namely the system operator side and the end-user side. Most of the given examples concentrated on solely the easier integration of non-expert end-users via easy-to-use mobile, etc. applications. However, to the best knowledge of the authors, none of the existing studies in the literature or in the current industry dealt in detail with the interface requirements of the system operator that should interact with the responses from all the end-user DR-driven mobile, etc. applications and might also have the capability to make or at least offer effective decisions on behalf of or to the system operator.  
Besides, none of the end-user oriented DR applications considered a dual interaction with the system operator and the possible HEM unit of the end-user. As the penetration of HEM systems is likely to increase with the higher levels of investment from the industry, such applications should also work interactively with such HEM systems apart from following the commands of or sending simple replies to a system operator.  
Therefore, this study tends to be the first in the literature in terms of bringing 'conceptual novelties', rather than 'technical contributions', in interactions between the system operator and end-user and bridging the system operator and decision making units (e.g., HEM systems) of end-user premises.
# Improving the Solving of Optimization Problems:
## 3 Quantum-Compliant Problem Formulations
### Qibo  
Quadratic Unconstrained Binary Optimization (QUBO) [13] models CO problems using a quadratic pseudo-Boolean objective function, similar to the Ising model but with unipolar variables \((0,1)\) instead of bipolar \((-1,1)\). A QUBO problem is typically expressed as follows:  
\[\text{minimize}\,f(\mathbf{x})=q_{0}+\sum_{i=1}^{N}q_{i}x_{i}+\sum_{i=1}^{N} \sum_{j=i+1}^{N}q_{ij}x_{i}x_{j}\,, \tag{2}\]  
where \(N\) is the number of variables, equivalent to the number of spins in the Ising case; \(x_{i}\) is the \(i\)th binary variable; \(q_{i}\) is the bias associated with the single \(i\)th variable; \(q_{ij}\) is the coupling coefficient between the \(i\)th and \(j\)th variables; and \(q_{0}\) is an offset, which can be neglected during optimization, as it does not influence the optimal configuration of variables. Alternatively, it can be written in matrix form:  
\[\text{minimize}\,f(\mathbf{x})=\mathbf{x}^{t}\mathbf{Q}\mathbf{x}\,, \tag{3}\]  
where \(\mathbf{Q}\) is a square matrix with linear coefficients on the diagonal and coupling coefficients off-diagonal, either in upper triangular or symmetric form.  
In both QUBO and Ising formulations, the constraints can be taken into account only through the quadratic penalty functions:  
\[\text{minimize}\,y=f(\mathbf{x})+\lambda g(\mathbf{x})\,, \tag{4}\]  
where \(\lambda\) is a positive penalty parameter and \(g(\mathbf{x})\) is the penalty function. Multi-objective optimization can also be handled in QUBO using an aggregation approach (Objective Weighting), which combines multiple objectives into a single one [22].  
The QUBO formulation can be translated in the Ising model by applying a simple change of variable:  
\[s_{i}=2x_{i}-1\,. \tag{5}\]  
From this equation, transformations to switch from QUBO to Ising can be straightforwardly performed.
# Management Platforms and Protocols for Internet of Things: A Survey
## 8 IoT Device Management Platforms
### Xively  
Xively provides an API for managing data from the sensors/devices through cloud services. It allows historical data and provides events based on the data generated by sensors/devices. It was created based on the EcoDiF platform [24], Based on REST principles and Web standards such as HTTP and URIs. To minimize the incompatibility among different devices, the platform provides standardized interfaces. The data is organized into data points, streams, and feeds. A feed represents an environment data (i.e., a room) with its data streams, representing data sent by a particular sensor in that environment (i.e., temperatures of the monitored environment).  
Xively is a commercial and closed source solution [106]. There are little details about the architecture of this platform, which is shown in Figure 28. The sensors send data to the platform in JSON, XML or CSV formats using the REST API, via sockets and through the MQTT protocol [107]. However, it is known that there are three ways to manage the devices connected to Xively. In the first case, through the methods implemented by HTTP, the GET method is used by a client program to retrieve data from a feed or data streams. The PUT method is used in the connected devices to send data. In the second case, a socket can be created to avoid the overhead of opening and closing HTTP communications under conditions in which too much data is exchanged. In addition, the first two cases allow the use of the SSL/TLS protocol to provide authentication and data encryption. Finally,  
Figure 27: Illustration of the RestThings architecture.  
the third case uses the publish and subscribe features of the MQTT protocol to send and retrieve data from devices.  
_Pros: Integrate with devices easily._  
_Cons: Displays the minimum device notification service._  
Use Case  
A scalable platform that addresses need residential customers and contractors were developed by SunStat Connect Thermostat, called Watts Water's SunTouch [108].  
The Xively IoT Platform is used to power the remote connectivity of SunStat thermostats, which work with all SunTouch heating products. Furthermore, the Xively provides nearly instantaneous response times from the devices, while not sacrificing stability and reliability that consumers would expect from the heating control device. The Xively platform developed remote connectivity in the SunStat application, enabling consumers to control SunStat devices from a Web browser or mobile device from anywhere in the world connected to the Internet.  
Another application is Blueprint [109], that consists of a scalable object directory model with a fast MQTT-based messaging broker. It has a secure provisioning process, which supports millions of secure connections between people, devices, and data around the world.
# Detecting Out-of-Distribution Samples in Complex IoT Traffic Based on Distance Loss
## 2 Related Work  
### OOD Detection  
OOD detection emerged as a critical research topic in deep learning following the introduction of the maximum softmax probability (MSP) baseline by [PERSON] and [PERSON] [10]. MSP identifies OOD samples based on the maximum softmax confidence, but tends to produce overconfident predictions even for anomalous samples, leading to suboptimal separability between ID and OOD samples. To address this limitation, ODIN [4] augments MSP with temperature scaling and small input perturbations to amplify the distinction between ID and OOD confidence scores. Although ODIN improves detection performance in certain cases, its effectiveness relies heavily on the choice of temperature and perturbation parameters, and may degrade in high-dimensional feature spaces.  
Beyond softmax-based approaches, energy-based models (EBMs) [12] compute the log-sum-exp of model logits to generate an energy score that replaces confidence-based indicators. EBMs offer a more theoretically grounded detection signal, but can still suffer from class overlap in the learned representation space. Autoencoder (AE)-based methods [11] have also been widely applied for OOD detection, where reconstruction error serves as the anomaly score. While effective in low-dimensional or structured domains, AE methods often struggle with complex or large-scale datasets due to limited capacity in modeling semantic feature relationships.  
[PERSON] et al. [13] propose using Mahalanobis distance in the feature space to estimate the likelihood of a sample belonging to known classes. This method considers feature correlations and provides robust scoring under certain conditions. However, it assumes Gaussian-distributed features, which may not hold in real-world IoT traffic, especially in scenarios with non-Gaussian or multi-modal feature distributions. These limitations highlight the need for more adaptable OOD detection strategies that can handle complex and imbalanced data, such as those commonly found in IoT environments.
###### Abstract  
In the research of passive millimetre wave (PMMW) imaging, the focal plane array (FPA) can realize fast, wide-range imaging and detection. However, it has suffered from a limited aperture and off-axis aberration. Thus, the result of FPA is usually blurred by space-variant point spread function (SVPSF) and is hard to restore. In this paper, a polar-coordinate point spread function (PCPSF) model is presented to describe the circle symmetric characteristic of space-variant blur, and a log-polar-coordinate transformation (LPT) method is propagated as the pre-processing step before the Lucy-Richardson algorithm to eliminate the space variance of blur. Compared with the traditional image deblur method, LPT solves the problem by analyzing the physical model instead of the approximating it, which has proved to be a feasible way to deblur the FPA imaging system.  
Keywords:focal-plane imaging; space-variant deblur; resolution recovery; log-polar-coordinate transformation; EMC detection +
Footnote †: journal: Applied Physics Letters