esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Neural architecture search (NAS) is an algorithm that automatically finds the optimal model architecture, and it is a field of automatic machine learning (AutoML), which has been recently gaining attention. NAS enables automatic design of the model architecture rather than manually running hyperparameters of models. NASNet [1], which laid the foundation of NAS, searches architecture with 500 GPUs in four days (1800 GPU-days) among \(10^{15}\) possibly generatable architectures. Therefore, current NAS algorithms have evolved to reduce the search space and select operations with changes in criteria to decrease the search cost and increase performance.  
Early NAS algorithms, including reinforcement learning [2] and evolutionary algorithms [1], had huge search costs, up to thousands and hundreds of GPU days, whereas gradient-descent-based DARTS [3] have shown a remarkable accuracy with only four GPU days over one 1 GPU compared to existing methods. DARTS uses the idea of a cell-based architecture defined in NASNet [1] to find the best network architecture. The final architecture found by DARTS has multiple stacks of the same struct cell, which is composed of multiple nodes. Each node is connected by operations selected from the candidate operations. DART aims to determine the shape of the directed acyclic graph (DAG) of the nodes composing the cell architecture, and to select the operations used on each edge. Mixed operations, which include every candidate operation, are generated and used on the edges connecting the nodes. With the progression of the training step, each operation comprisinga mixed operation is changed. When the training is completed, one of the operations that has the largest architecture parameter is selected, and then the cell architecture is decided. The stacks of the cells comprise the final model.  
The current DARTS experiences the problem of vote dispersion, as described in Section 3.1. In this study, our goal was to solve the vote dispersion problem in DARTS in order to reduce the error in the final model and decrease the GPU cost. Therefore, we propose differential group-differentiable architecture search (DG-DARTS ) in this paper.  
The rest of this article is organized as follows: Section 2 describes related works, especially focusing on DARTS. Section 3 provides a description of DARTS and discusses previous works, along with their relationships. In Section 4, we present the experimental environment details and results. Section 5 discusses the major benefits of our research in comparison to existing results. Finally, in Section 6, we conclude the paper.
## 5 Results
### Comparative Study with Different LLMs  
To clearly demonstrate and verify the advantages of the GLR framework, we conduct comparative experiments using four representative off-the-shelf LLMs, namely Qwen-7B-Chat with 7 billion parameters [50], its 4-bit quantized version Qwen-7B-Chat-int4 [50], LLaMA2-7B-Chat with 7 billion parameters [51], and GPT-3.5-Turbo with 175 billion parameters [48]. We perform experimental analysis on three public benchmark datasets, which are FB15K-237, WN18 RR, and UMLS. The experimental results are presented in Figure 5.  
The medium-scale model Qwen2-7B, enhanced by the GLR framework (referred to as GLR-Qwen2-7B), achieves the best performance across all three datasets, with MRR scores of 0.679 on WN18 RR, 0.507 on FB15K-237, and 0.804 on UMLS. It significantly outperforms all original LLMs without the GLR framework. These comparative results demonstrate that GLR exhibits strong performance compensatory capabilities, effectively improving downstream task performance. GLR-Qwen2-7B consistently achieves the best results on three datasets with distinct characteristics, namely the general domain (FB15K-237), the lexical domain (WN18 RR), and the medical domain (UMLS), indicating the excellent cross-context generalization ability of the GLR framework. By introducing explicit reasoning paths through Graph-CoT, GLR-Qwen2-7B transforms the original LLM from a generative search paradigm to a structure-guided selection paradigm. This transformation significantly reduces generation errors and improves the stability and reliability of predictions. These findings verify that structure-enhanced reasoning is more effective than merely increasing model parameters in improving KGC performance.  
Meanwhile, we further compared the differences in runtime efficiency and memory consumption across methods. Under the same hardware environment (NVIDIA L20 GPU), GLR-Qwen2-7B required 20 minutes to complete training on the UMLS dataset, representing an improvement of approximately 39.3% in training efficiency compared to the average training time of 33 minutes observed for other vanilla LLMs. For the WN18 RR and FB15K-237 datasets, the training time was reduced by approximately 17.6% and 21.7%, respectively. In addition, since LoRA updates only a small subset of model parameters, the number of trainable parameters in GLR is reduced by approximately 97%, which leads to a substantial decrease in memory usage. These findings further demonstrate that the GLR framework delivers not only significant performance improvements but also superior cost-effectiveness in terms of computational resources.
# Anticipatory Troubleshooting
## 2 Traditional Troubleshooting
Consider the case where the cost of sense actions is much smaller than the cost of repair actions. This simplifies the troubleshooting process: perform sense actions on components until a faulty component is found, and then repair it. The challenge in this case is which component to sense first. To address this challenge, efficient troubleshooting algorithms use a diagnosis algorithm (DA). A DA outputs one or more diagnoses, where a diagnosis is a hypothesis about which components are faulty. These components will be sensed first. Model-based diagnosis (MBD) is a classical approach to diagnosis in which a model of the system together with observations of the system's behavior are used to infer diagnoses. Some MBD algorithms assume a system model that represents the system's behavior using propositional logic. They use logical reasoning to infer diagnoses that are consistent with the system model and observations [7; 8; 9; 10; 11; 12; 13]. In this work, we assume the available model of the system is a Bayesian network (BN) that represents the probabilistic dependency between observations and the system's health state [14]. Briefly, such a BN consists pf three disjoint sets of nodes: (1) control nodes, whose values depend only on the user's actions; (2) sensor nodes, whose values represent data that can be observed from the system sensors; and (3) health nodes, whose values represent the state--healthy or some faulty state--of the system's components. The observations of the system's behavior are assignments of values to the control and at least some of the sensor nodes. A diagnosis is an assignment of values to the health nodes. The likelihood of a diagnosis \(\omega\) can be computed by running a BN inference algorithm such as variable elimination to compute the marginal probabilities of the health node assignments represented by \(P(\omega)\). The probability of each component \(C\in\omega\) to be faulty, denoted by \(P(C)\), also can be derived [15].
## 5 Evaluation
### Impacts of Experimental Parameters
#### 5.6.3 Impact of Additive White Gaussian Noise (AWGN)  
We manually added extra AWGN to the calculated RSSI to validate the stability of our solution. The value of the AWGN \(\mathbf{N}\) (dB) was controlled by the standard deviation \(\sigma\) for \(\mathbf{N}\sim\mathcal{N}(0,\sigma^{2})\). One may note that the extra AWGN here was not caused by obstacles in the environments, which were already recorded by the channel matrices in the DeepMIMO dataset and implicitly represented by the calculated RSSI. We investigated AWGN here for simulation of device noise to further refine our results. Numerical results are presented in Table 5.  
We investigated the performances when \(\sigma=0/5/10\). We kept \(C=144\) and \(A=51.84\). Figure 19 shows the location-estimation errors of the oracle/baseline cases and our solution for different AWGN values. It can be seen that our solution performs much better than the baseline cases and is more similar to the oracle cases.
## 4 Modeling and Analysis
### Modeling for General Relay-Node Selection on Intersection
#### 4.3.1 Metrics in Intersection Phase  
In Intersection Phase, our proposal finds the nearer node to C than H in the circle region centered at C with radius of \(D_{\overline{HC}}\). In addition, H is \(R/2\) from C. Therefore, the optimal position \(P_{\text{opt}}\) is C, and \(R_{\text{p}}=R/2\). Moreover, since the circle region covers \(N_{\text{b}}\) branches, \(\hat{\lambda}=N_{\text{b}}\lambda\), where \(\lambda\) is the vehicle density in a branch. From (8) and (13), we can obtain the partition latency \(T_{\text{part\_I}}\) and the contention latency \(T_{\text{con\_I}}\) in Intersection Phase as  
\[T_{\text{part\_I}}=T_{\text{part}}\left(N_{\text{b}}\lambda,\frac{R}{2}\right) =\left(\left(\sum_{j=1}^{N_{\text{neg}}}\sum_{i=1}^{N_{\text{neg}}(j)}\left(N_ {\text{p\_slot}}(i)P_{\text{seg\_sel}}^{j,i}\left(N_{\text{b}}\lambda,\frac{R}{ 2}\right)\right)\right)+1\right)T_{\text{slot}} \tag{16}\]  
\[T_{\text{con\_I}}=T_{\text{con}}\left(N_{\text{b}}\lambda,\frac{R}{2}\right) =\sum_{i=1}^{N_{\text{neg}}(N_{\text{bar}})}T_{\text{con\_seg}}^{i}\left(N_{ \text{b}}\lambda,\frac{R}{2}\right)P_{\text{seg\_sel}}^{N_{\text{bar}},i}\left( N_{\text{b}}\lambda,\frac{R}{2}\right) \tag{17}\]  
and message progress \(\beta_{\text{I}}\) and \(PDR_{\text{I}}\) in Intersection Phase from (14) and (15) as  
\[\beta_{\text{I}}=\beta\left(N_{\text{b}}\lambda,\frac{R}{2}\right)=\sum_{i=1}^ {N_{\text{neg}}(N_{\text{bar}})}M_{i}P_{\text{seg\_sel}}^{N_{\text{bar}},i} \left(N_{\text{b}}\lambda,\frac{R}{2}\right) \tag{18}\]  
\[PDR_{\text{I}}=PDR\left(N_{\text{b}}\lambda,\frac{R}{2}\right)=1-\sum_{i=1}^{ N_{\text{neg}}(N_{\text{bar}})}\left(\left(\prod_{c=1}^{N_{\text{pos}}}p_{ \text{col\_con}}^{i,c}\left(N_{\text{b}}\lambda,\frac{R}{2}\right)\right)P_{ \text{seg\_sel}}^{N_{\text{bar}},i}\left(N_{\text{b}}\lambda,\frac{R}{2} \right)\right) \tag{19}\]
# Applications of Nanomaterials in RFID Wireless Sensor Components
## 5 Applications
### Others  
In addition, RFID sensor systems are widely utilized in various fields such as stock management, railway, shipping, electronic toll systems, automotive, and aerospace. With the expanding applications of RFID technology, there is an increasing demand for efficient measurement of physical and chemical parameters, as well as diverse information collection and processing in different environments. Consequently, there is a growing movement to enhance the performance of RFID components using nanomaterials. As an example, [PERSON] et al. suggested an efficient inventory management through the implementation of smart packaging using a paper substrate and highly compatible silver ink [151]. They  
Figure 13: (**a**) Passive HF RFID tag sensor for soil moisture measurement. (**b**) Humidity detection capability of a fully printed RFID sensor utilizing pHEMA [100]. (**c**) Operation of a passive RFID multidimensional integrated sensor for humidity, temperature, light, and CO\({}_{2}\) detection [121]. (**d**) Leaf moisture monitoring using graphene active film [113].  
facilitated the potential of smart boxes capable of effectively managing large inventories by connecting high-frequency RFID chips to the screen-printed conductive adhesive on paper. [PERSON] et al. attempted to improve the performance of passive wireless sensors used in the space industry through Kapton wrapping of the antenna conductive surface [152]. As a result, the sensor achieved a detection range of up to 3.2 m and a mean measurement error of less than 1%. These efforts would lead to broader applications of RFID technology. Additionally, with the increasing demand for user-friendly electronic devices, research is actively progressing to advance RFID sensor components, ensuring their superior performance and stable operation even in wearable environments. [PERSON] et al. reported on the impact of the sintering process on RF antenna performance after printing conductive ink using the pad printing method [153]. The sintered printed sensor enabled wireless data transmission at shorter range and demonstrated stable operation in wearable and moisture-sensitive environments. In this way, research is actively underway to implement diverse nanomaterials and manufacturing methods for RFID systems to be applied in various fields.
## 5 Automatic Signal Recognition
### Sample Algorithm  
The sample algorithm is summarized in pseudo-code as shown in Algorithm 1. First, Wi-Fi is recognized if the RSSI contains a large amount of noise and has s sufficient standard deviation; the latter condition is used to distinguish Wi-Fi signal from noise. Next, the LTE signal is identified when the standard deviation is above a certain threshold. Then, when the previous condition is not met and the number of peaks in the RSSI histogram is below a certain threshold, the signal is considered to be DVB-T, otherwise, it is classified as unknown. The sample algorithm relies on several thresholds, which are derived via an independent training dataset in the following way. Note that this is essentially a process to \"learn\" the optimal threshold with a supervised algorithm.  
* \(\theta_{\bar{n}}\) is the upper bound of the average noise level, obtained by the maximum of \(\overline{n}\) in the collected Wi-Fi traces plus the standard deviation of \(\overline{n}\).
* \(\theta_{p}\) determines the minimal amount of noise present in the Wi-Fi's RSSI measurements. It is calculated by the smallest 'noise peak' minus the standard deviation of the noise peaks in the Wi-Fi's RSSI measurements.  
Figure 10: The normalized histograms of packet duration of Bluetooth and Wi-Fi, derived by applying threshold of \(-65\) dB to the RSSI traces, which are obtained at a 1-MHz sampling rate and averaged over 20 μs. The scale of the graph is zoomed to the range [0, 800] μs for visualization purposes.  
* \(min(Wi\)-\(Fi.\sigma_{\overrightarrow{R}})\) denotes the minimum standard deviation among the collected RSSI measurements of Wi-Fi, which is used to differentiate Wi-Fi from noise.
* \((min(LTE.\sigma_{\overrightarrow{R}})+max(DVB\)-\(T.\sigma_{\overrightarrow{R}}))/2\) denotes the medium of the minimum and the maximum standard deviation of LTE and DVB-T's RSSI measurements. It is used to differentiate LTE and DVB-T signal.
* \(max(DVB\)-\(T.N_{p})\) denotes the maximum number of peaks in the histograms of the RSSI measurements of DVB-T. It is used to exclude unknown signals from the DVB-T signals.  
```
0:\(\overrightarrow{R}\) // a vector of RSSI measurements
0:
1:\(\bigtriangleup\) derive variables from \(\overrightarrow{R}\) \(H\gets Histogram(\overrightarrow{R})\) \(\sigma_{\overrightarrow{R}}\gets StdDeviation(\overrightarrow{R})\) \(\overrightarrow{n}+LocOfLeftmostPeak(\overrightarrow{H})\) \(p(\overrightarrow{n})\gets AmpOfLeftmostPeak(\overrightarrow{H})\) \(N_{p}\gets TotalNumOfPeaks(\overrightarrow{H})\) // / process to make decision if\(\overrightarrow{n}\leq\theta_{n}\)\(\delta\)\(p(\overrightarrow{n})\geq\theta_{p}\)then //
```  
**Algorithm 1** RSSI distribution-based technology recognition.
## 4 Areas that Require Further Research
### Use of passive microwaves for moisture status
#### Data Fusion
As noted, single date imagery might suffice (if acquired at right time of crop phenology) to map irrigated areas at local scales (e.g., corn _vs._ soybeans during critical growth phases in U.S. mid-west) but when the extent of mapping in regional/continental/global this will be highly inadequate. Here is where data fusion, along with non-optical data from microwave remote sensing and numerous secondary data, included in data fusion, will be invaluable.
## 2 Experimental Study
### Experimental Results
This may be related to the water condensation, the phase change from vapour to liquid, and the capillary effect in the small pores at low porosity. The condensed water bridges the void spaces between soil particles. With increasing porosity, the pore size will increase correspondingly, and, as a result, the capillary condensation effect will decrease. When many of the pores have reached a critical value in size, the capillary effect will become too small to be easily noticed. This observation is supported by the 15% water content result, which shows that the thermal conductivity decreases with the increase of porosity in an approximate straight line (needle probe method), when there is little variation in the capillary condensed water, and increase in the air content with the increasing porosity. Figure 5: The thermal conductivity vs. porosity at different water contents. Figure 6 shows the variation of the thermal conductivity, measured using the needle probe method, against the water contents and degrees of water saturation at different porosities. It can be seen that initially with water increases from 0% to a relatively low content (10%), the thermal conductivity increases in an approximately linear trend. Thereafter the linear increasing trend starts to decrease until the content of 15%. The initial linear increase of thermal conductivity with the increase of water content from 0% to 10% can be explained by the fact that the initial water forms an evenly distributed water film coating on all pore surfaces of a constant surface area. The initial water content increase corresponds to the water film thickness increase which gives a linear relation to thermal conductivity. When the water reaches a certain level, more pore water occupies the pore volume space due to the capillary condensation. In this situation, the ratio of the water volume to the pore surface area decreases, and, as a result, the rate of the thermal conductivity increase drops off because the increased mass of water increases the local thermal capacity. Theoretically, the thermal conductivity will reach a constant value when soil becomes fully saturated. Figure 6 shows that the thermal conductivity is slightly higher at the water content of 20% than 15%. This can be explained that in the range of 15%-20%, free water starts to occupy the central region of the pore spaces up to full saturation, a situation in which the thermal conduction is further enhanced by a better connected pore water network.
# AI-Supported EUD for Data Visualization: An Exploratory Case Study
## 1 Introduction
In particular, our exploratory case study research allowed us to answer the above overarching question by digging into two more specific research questions (RQs) and work hypotheses:  
_RQ1: Who are the workers (role, responsibility, competency) performing data analysis and visualization tasks in an organization, and how are their tasks characterized (technologies, literacy, business strategies)?_  
_RQ2: Which behaviors and expectations would emerge if visual analytics and data viz design practices were enhanced with an AI-supported EUD tool?_  
The first phase of the case study research aimed at answering RQ1. It consisted of a qualitative investigation carried out by means of interviews with key informants, whose role is to modify data visualizations or to create new ones. This phase led to identifying key user profiles that require different levels of support and personalization to carry out their work with the use of data viz. Based on the results of this phase, a second phase of the case study research aimed at answering RQ2. It consisted of developing an LLM-enabled EUD environment that allowed users to create customized data visualizations that meet their different needs, skills, and work practices, and of the direct observation of a selection of key informants interacting with this prototype. This LLM-enabled environment can be defined as a design probe, rather than a classical prototype [7]. The difference between the two is crucial for our purpose. A full prototype is subject to users' evaluations regarding usability, with the goal of developing and deploying it in the long run. A design probe is purposely designed as an underspecified artifact whose main goal is to be used in everyday practices to challenge, engage, and solicit reactions and responses from the users. For our intent, this probe should bring hypotheses about which design tradeoff between the adaptability and adaptivity of AI-supported EUD tools can be formulated. In this second phase, qualitative data were collected through direct observation and a questionnaire, where selected users provided their opinions about the effectiveness and trustworthiness of LLM-powered interactions. The exploratory case study research finally led us to hypothesize design guidelines for AI-supported EUD tools for visual analytics and data viz design practices. The paper is structured as follows: Section 2 gives an account of previous works in the domain of data viz in organizational contexts and EUD approaches adopted in this domain; Section 3 describes the framework for our exploratory case study analysis in the data viz domain, and presents the analysis and the outcomes of the qualitative interviews; Section 4 presents our LLM-based EUD environment and the results of the experimental interaction with key users; Section 5 reports the findings, highlighting the potential and limitations of our approach; Section 6 concludes the paper and outlines possible future work.