esa-sceva/satcom-chunk-collection huggingface dataset:
# Entropy-Guided Distributional Reinforcement Learning with Controlling Uncertainty in Robotic Tasks
## 1 Introduction
In the distributional RL, the discount factor is essential in scaling the reward distribution, making it more effective than the generic hyperparameter tuning. Our approach involves evaluating the reliability of the future reward data based on policy entropy and adjusting the discount factor accordingly resulting in enhanced stability and data efficiency. In our evaluation of the EG-TQC method using robotic tasks in the Mujoco environment of the DeepMind Control Suite, we found that it eliminates the need to search for the optimal discount factor for each environment. Additionally, it outperforms the TQC algorithm trained with various fixed discount factors. Furthermore, we extended the evaluation to more complex and higher-dimensional environments, specifically the robotic arm control tasks provided by Gymnasium robotics environments. Using our proposed method in combination with a Hindsight Replay Buffer [25], we demonstrated that the EG-TQC method performs exceptionally well, even in high-dimensional control environments. The key contributions of this paper are as follows:  
* Policy entropy-based adaptive discount factor adjustment: In conventional reinforcement learning algorithms, the discount factor (\(\gamma\)) is fixed, limiting its adaptability to different environments and learning states. In this study, we propose a method that dynamically adjusts the discount factor based on policy entropy, allowing the agent to reflect its learning state. This approach enables the agent to prioritize future rewards in stable learning conditions while focusing on short-term rewards in highly uncertain situations. Integration of distributional RL with adaptive discount factor adjustment:  
We integrate the proposed entropy-based adaptive discount factor adjustment into the TQC algorithm, enhancing its robustness in highly uncertain environments. This improvement allows the algorithm to achieve better learning efficiency and maintain stable performance across various environments compared to standard TQC. * Empirical validation in robotic environments:  
To verify the performance of EG-TQC, we conducted extensive experiments in the Mujoco environment of the DeepMind Control Suite and the Gymnasium robotic control environments. The results show that EG-TQC achieves an average of 11% higher evaluation returns compared to TQC with fixed discount factors. Additionally, experiments incorporating Hindsight Replay Buffer demonstrated the generalizability of the proposed method, proving its superior performance and stability in high-dimensional environments.
###### Abstract  
Authentication algorithms in the form of cryptographic schemes, such as the Secure Hash Algorithm 1 (SHA-1) and the digital signature algorithm (DSA), specified in the current variable message format (VMF) military standard have numerous reliability-related limitations when applied to tactical data link (TDL) and multi-TDL networks (MTN). This is because TDL and MTN require maximum tactical security, communication integrity, and low network overhead based on many protocol header bits for rapid communication with limited network resources. The application of such authentication algorithms to TDL and MTN in a rapidly changing battlefield environment without reinforcement measures will lead to functional weaknesses and vulnerabilities when high-level digital-covert activities and deception tactics are implemented. Consequently, the existing VMF authentication scheme must be improved to secure transmission integrity, lower network transaction, and receive authentication tactical information in VMF-based combat network radio (CNR) networks. Therefore, in this study, a tactical wireless ad hoc network topology, similar to that of the existing CNRs, is considered, and a lightweight multi-factor hash chain-based authentication scheme that includes a time-based one-time password (T-OTP) for network overhead reduction and terminal authentication is proposed, coupled with exception handling. The proposed method enhances the confidentiality of tactical message exchanges and reduces unnecessary network transactions and transmission bits for authentication flows between real-time military terminals owned by squads, while ensuring robustness in limited battlefields. Based on these approaches, in the future, we intend to increase the authentication reliability between wireless terminals in the Korean variable message format (KVMF)-based CNR networks based on the Korean Army Corps network scenarios.  
military; VMF; hash chain; T-OTP; lightweight secure hash (LSH); CNR
## 3 Case Study
### TEP Dataset Analysis
#### 3.1.4 Efficacy of Task Sequencing in ATASML  
This investigation assesses three meta-learning frameworks: MAML, GOPML and ATASML, each distinct in their approach to task sequencing. MAML serves as a foundational framework that does not prioritize task sequencing. GOPML, conversely, uses gradient information to enhance task selection and ordering, thereby improving specificity and efficiency in learning processes. The nuances and relative advantages of GOPML are detailed further in our prior publications [45].  
Empirical evaluations were conducted using the TEP dataset to measure the performance of the algorithms across varied fault classification tasks. The focus was on key metrics such as Accuracy, F1 Score and Precision, which together assess the efficacy of each model in fault classification. These metrics are crucial for evaluating the models' ability to accurately identify faults and minimize false positives. The results are comprehensively presented in Table 5.  
Figure 6: Performance comparison demonstrating the superior adaptability and accuracy of the ATASML framework against traditional models like VGG-11 [43] and ResNet-18 [44] under few-shot fault-diagnosis scenarios.  
The data distinctly demonstrate ATASML's superior performance in utilizing task sequencing to optimize the training process. Notably, ATASML achieves an accuracy of 98.47% in the 3-way 6-shot setting and maintains a high accuracy of 90.13% in the more complex 8-way 6-shot scenario. These findings underscore ATASML's enhanced efficiency and accuracy in handling complex fault-diagnosis situations compared to MAML and GOPML. For instance, in the 8-way 5-shot configuration, ATASML registers an F1 Score of 82.88%, significantly surpassing GOPML's 75.92% and MAML's 64.90%. The precision metrics across all configurations also indicate ATASML's superior performance, highlighting its effectiveness in minimizing false positives.
# Cramer-Rao Lower Bounds on 3D Position and Orientation Estimation in Distributed Ranging Systems
## 3 Bounds on Position Estimation  
In this section, we define the ranging model, derive 2D and 3D CRLBs on position estimation, and discuss geometric dilution of precision (GDoP).
## 4 Experiments and Results
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Sensitivity**} & \multicolumn{1}{c}{**FP/Scan**} & \multicolumn{1}{c}{**0.125**} & \multicolumn{1}{c}{**0.25**} & \multicolumn{1}{c}{**0.5**} & \multicolumn{1}{c}{**1**} & \multicolumn{1}{c}{**2**} & \multicolumn{1}{c}{**4**} & \multicolumn{1}{c}{**8**} & \multicolumn{1}{c}{**Average CPM**} \\ \hline \multirow{3}{*}{**V1**} & [PERSON] et al. (2010) & 0.678 & 0.738 & 0.816 & 0.848 & 0.879 & 0.907 & 0.922 & 0.827 \\  & UACNN (2010) & 0.655 & 0.745 & 0.807 & 0.849 & 0.880 & 0.907 & 0.925 & 0.824 \\  & [PERSON] et al. (2015) & 0.692 & 0.771 & 0.809 & 0.863 & 0.895 & 0.914 & 0.923 & 0.838 \\  & [PERSON] et al. (2019) & 0.760 & 0.794 & 0.833 & 0.860 & 0.876 & 0.893 & 0.906 & 0.846 \\  & [PERSON] et al. (2018) & 0.880 & 0.894 & 0.907 & 0.912 & 0.914 & 0.919 & 0.927 & 0.908 \\  & MSH-CNN (ours) & 0.759 & 0.812 & 0.859 & 0.901 & 0.917 & 0.928 & 0.943 & 0.874 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Competitive performance metric (CPM) scores of different methods on V1 dataset of LUNA16. We also tested performance on the large-sized V2 dataset as presented in Table 4. The proposed 3D method was better than other methods at high FP/scan. Specifically, the average CPM scores of our method and the sensitivity at different FP/scan were higher than those of [PERSON] et al. [5]. In comparison with [PERSON] et al. [8], the sensitivity of our network at 0.125, 0.25, and 0.5 FP/scan was lower, but it still achieved the best performance at the 1, 2, 4 and 8 FP/scan. It is worth noting that, comparing the performance of various algorithms on the V1 and V2 datasets, we can see that our sensitivity was higher than that of [PERSON] et al. [8] in the false-positive rates of range 1-4 which are commonly used in clinical applications [30], and, as the dataset increased, our 3D method performed better and better at low FP/scan, even exceeding that of [PERSON] et al. [8] at the 1 FP/scan. In terms of average CPM, [PERSON] et al. [5], [PERSON] et al. [8], and our method increased by 0.016, 0.034, and 0.055, respectively, and our 3D network had the best improvement.
# Beyond Manual Media Coding: Evaluating Large Language Models and Agents for News Content Analysis
###### Abstract  
The vast volume of media content, combined with the costs of manual annotation, challenges scalable codebook analysis and risks reducing decision-making accuracy. This study evaluates the effectiveness of large language models (LLMs) and multi-agent teams in structured media content analysis based on codebook-driven annotation. We construct a dataset of 200 news articles on U.S. tariff policies, manually annotated using a 26-question codebook encompassing 122 distinct codes, to establish a rigorous ground truth. Seven state-of-the-art LLMs, spanning low- to high-capacity tiers, are assessed under a unified zero-shot prompting framework incorporating role-based instructions and schema-constrained outputs. Experimental results show weighted global F1-scores between 0.636 and 0.822, with Claude-3-7-Sonnet achieving the highest direct-prompt performance. To examine the potential of agentic orchestration, we propose and develop a multi-agent system using Meta's Llama 4 Maverick, incorporating expert role profiling, shared memory, and coordinated planning. This architecture improves the overall F1-score over the direct prompting baseline from 0.757 to 0.805 and demonstrates consistent gains across binary, categorical, and multi-label tasks, approaching commercial-level accuracy while maintaining a favorable cost-performance profile. These findings highlight the viability of LLMs, both in direct and agentic configurations, for automating structured content analysis.  
large language models; media content analysis; codebook annotation; multi-agent systems; agentic
# An Optimal Authentication Scheme through Dual Signature for the Internet of Medical Things
## 5 Proposed Dual Signature Scheme
### Setup  
Here, the central authority (CA) can select \(\mathcal{L}\) as a hash function and genius 2 hyperelliptic curve with 80 bits key size and its finite field \(F^{n}\), where \(n\geq 2^{80}\). Then, it publishes these parameters to the network publicly.
## 3 Research Methodology
### Identify Problems/Research Gaps (Phase A)  
In this first phase, we reviewed the existing works on the domain-specific requirement of WSC selection techniques, IoT, and large-scale databases. Based on the existing work on service selection methods, we identified those existing approaches incapable of giving a good performance, especially for a large-scale repository that can lead to high computation time in dynamic WSC.  
We also reviewed methods in selecting the functional attributes of correct services since these were often overlooked by the researcher. Most of the proposed approaches only focus on Quality of Service (QoS), which is a non-functional attribute of services such as the response time. Focusing only on the QoS attributes can cause incompatibility of selected services during service composition. This can also violate user requirements and reduce customer satisfaction.
## 3 Theoretical Background
### Singular Value Decomposition (SVD)  
SVD is the decomposition of a real or complex matrix. If M is an image with dimension N \(\times\) N, then, it can be decomposed into two orthogonal matrices (like U and V) and one diagonal matrix S, such that \(M=USVT\)[27]. The elements of diagonal matrix S are called singular values. SVD can be widely used in several digital signal processing operations, including image restoration, data compression, power spectrum estimation, and noise reduction [28; 29; 30]. This SVD can be used in watermarking effectively, as this decomposition maintains the imperceptibility of the watermarked image properly [31]. Also, unique, singular values of SVD make it more robust against geometric [12] and other attacks. Besides, these singular values contain major information of an image [14]. Also, small changes due to noise cannot affect the singular values significantly [18]. These properties  
Figure 2: A 4L DWT on LL sub-band.  
make SVD more attractive to researchers in doing research with image watermarking. Motivated by this, we performed SVD after completing DWT on the host image.
## 3 Uncertainty Quantification of Macro Elastic Parameters Based on Non-Probabilistic Thought  
In Section 2, we have introduced some deterministic micro- and macro-mechanical analysis models for macro elastic constants including \(E_{1},E_{2},v_{12}\) and \(G_{12}\) of composite laminates, where the relationships between macro elastic parameters and micro input parameters of constituents or the explicit functions are established at the micro scale. However, it should be noted that these micro input parameters are uncertain owing to their inherent dispersions and complicated fabricating processes. As a consequence, the following formula can be obtained mathematically as:  
\[y=f(\mathbf{b})=f(b_{1},b_{2},\cdots,b_{m}) \tag{9}\]  
where \(y\) is the macro elastic mechanical parameters of composites including \(E_{1},E_{2},v_{12},G_{12}\) and \(\mathbf{b}\) is the uncertain micro input vector including material properties of constituents, volume fractions and model parameters, etc. \(f(\cdot)\) is the analytical formula or explicit function originating from different quantification models. Generally speaking, the uncertain vector \(b\) can be treated as probabilistic random variables [21,29], whose probability density distributions have been given or are known. Here, in order to guarantee that uncertainty quantification of macro elastic parameters for composites is convinced and correct, probability distributions must be precisely modeled. However, in practice, it is always difficult to obtain sufficient sample points. In particular, when available data is limited or insufficient so that uncertain input micro parameters are not appropriately represented with probability distributions, those non-statistic quantification methods will be better choices. Typically, on the basis of non-probabilistic thoughts, uncertain input vector \(b\) can be treated as interval variables, namely:  
\[\underline{b}\leq\underline{b}\leq\overline{b} \tag{10}\]  
or the component form  
\[\underline{b_{i}}\leq b_{i}\leq\overline{b_{i}}\ \ i=1,2,\cdots,m \tag{11}\]  
Here, only lower and upper bounds of uncertain input parameters should be known. \(\underline{b}\) and \(\overline{b}\) are the corresponding lower and upper bounds, respectively. \(b_{i}\) is the \(i\)-th component of the vector. \(\underline{b_{i}}\) and \(\overline{b_{i}}\) are the lower and upper bounds, respectively. \(m\) is the number of uncertain input parameters at the micro scale. According to the theory of interval mathematics, the formula (10) can be rewritten as the following interval form as:  
\[\mathbf{b^{I}}=[\underline{b},\overline{b}]=[\mathbf{b^{c}}-\mathbf{b^{r}},\mathbf{b^{c}}+\bm {b^{r}}]=\mathbf{b^{c}}+\mathbf{b^{r}}[-1,1] \tag{12}\]  
where the interval nominal value is \(\mathbf{b^{c}}=\frac{\overline{b}+\underline{b}}{2}\) and the interval radius is \(\mathbf{b^{r}}=\frac{\overline{b}-\underline{b}}{2}\). Furthermore, one can take the use of interval arithmetic operations to carry out non-probabilistic uncertainty quantification by combining with micro- and macro-mechanical analysis models.
## 6 Discussion
### Limitations  
We conducted our study inside a room and participants could rest their hands while holding the AR device on the table to produce the gestures. It will be interesting to investigate if users would produce the same interactions in other scenarios, like standing or even walking. We used a non-functional fabric-based fingerless glove-based prototype in our study. Similarly, we excluded pronation/supination of the forearm in our design because our participants did not prefer these movements and mentioned difficulties associating them with the given 27 HAR tasks during the series of pilot studies. Despite the absence of interactive functionalities and a set of distinct wrist movements, we were still able to understand users' behavior and responses to it as an input interface for HAR interactions. None of our participants were lefthanded and, thus, our gesture set is only suitable for righthanded users.
## 2 Structure of PV Panel and Causes of Power Loss
### Power-Loss Factors of PV Panels
#### 2.2.2 Power Loss Caused by Solar Altitude Change and Tracking System  
The altitude of the sun changes from sunrise to sunset and over different seasons. PV panels are most efficient when they are positioned perpendicular to the position of the sun; thus, it is efficient to use a solar tracking system. The methods for positioning panels at the optimal angle include tracking the sun through a software program, utilizing an optical sensor, or employing both methods. In a previous study, the use of a tracking system for one month led to an increase in the output power of a 100 W panel of approximately 18% compared to a case where no tracking system was used. Nevertheless, the implementation of the corresponding systems required additional structures and power systems and incurred maintenance costs and additional power consumption.
# Optimization of Image Capture Distance for Facial Thermograms in Dentistry
## 3 Results
### Temperature of the Region of Interest (ROI) in the Image  
The differences in the mean temperatures recorded in the ROI for the front view were 32.93 \(\pm\) 1.05 \({}^{\circ}\)C, 34.07 \(\pm\) 0.86 \({}^{\circ}\)C, and 33.62 \(\pm\) 1.06 \({}^{\circ}\)C at 15 cm, 30 cm, and 50 cm, respectively (Table 3). The mean ROI measured in the left profile view was 32.83 \(\pm\) 2.55 \({}^{\circ}\)C, 32.83 \(\pm\) 1.15 \({}^{\circ}\)C, and 32.57 \(\pm\) 0.87 \({}^{\circ}\)C for the different shooting distances of 15 cm, 30 cm, and 50 cm, respectively (Table 3). In the right view, the mean ROI recorded was 33.06 \(\pm\) 0.80 \({}^{\circ}\)C, 32.85 \(\pm\) 0.96 \({}^{\circ}\)C, and 32.35 \(\pm\) 0.93 \({}^{\circ}\)C at 15 cm, 30 cm, and 50 cm, respectively (Table 3).  
The differences in the mean temperatures at the ROI for all different shooting distances within in each profile view were statistically significant [Front: H(2) = 80.176, \(p\) < 0.001; Left: H(2) = 21.399, \(p\) < 0.001; Right: H(2) = 49.451, \(p\) < 0.001; Table 4]. Pairwise comparisons revealed significant differences in the mean temperature at ROI between all of the distance comparisons within all profile views (\(p\) < 0.001, \(p\)-value: Bonferroni; Table 5), except in the 30 cm-15 cm comparisons in both the left (\(p\) = 0.789, \(p\)-value: [PERSON]; Table 5) and right (\(p\) = 0.132, \(p\)-value: Bonferroni; Table 5) views.
# Enhancing Conversational Agent Development Through a Semi-Automatization Development Proposal
## 5 Application Example
### Technological Elements  
This section provides an overview of the technological ecosystem used to define, develop, and implement the conversational agent in this case study. The operating system, platforms, programming languages, frameworks, and other pertinent details are provided.  
* Operating system: Ubuntu 20.04 (LTS) 64 bits.
* Programming language: Java.
* Framework: Xatkit.
* Natural language processing service: Dialogflow/NLPJS.
* Middleware: Twilio API.
* Instant messaging service: WhatsApp.
* User query alternative: GPT-3 model text-curie-001, TheoKanning/openajava.
* Computing equipment: 4 GB RAM, 2 AMD vCPUs 1996 MHz shared, 80 GB NVMe SSD storage.  
In order to facilitate the implementation process, the Ubuntu 20.04 (LTS) x64 operating system was selected as it aligned with the case study and allowed for a realistic replication of a typical development environment. Frequently, Ubuntu is utilized as a web server for such projects. Furthermore, the Xatkit framework and the implementation of the Xatkit WhatsApp via the Twilio platform were employed to develop the conversational agent. The primary rationale for this selection was that this version is the one required by TheoKanning/openai-java to facilitate communication with the OpenAI API and thereby utilize GPT-3.  
One of the most significant advantages of Xatkit is its ability to integrate and modify the NLP provider at any stage of the development process, due to its utilization of MDD techniques. Consequently, it is not necessary to designate a specific one in the design stage. This has the advantage of allowing experimentation once the agent is developed, thus enabling the selection of the option that best suits the developer's needs or, alternatively, the option that demonstrates superior performance for the specific use case. In accordance with the aforementioned point, at this juncture of the case study, the natural language provider remains unspecified. Subsequently, one of the two available providers in Xatkit had to be selected. The two principal options were Dialogflow and NLP.JS. The regular expression parser was not considered suitable for inclusion in this study, as its primary function is for testing purposes. Similarly, the Xatkit NLU Engine was not selected for use, given that it is still in an experimental phase.
# Construction of Air Quality Level Prediction Model Based on STEPDISC-PCA-BP
## 3 Results and Discussion
### STEPDISC-PCA-BP, PCA-BP, and BP Model Performance Evaluation  
In order to objectively and accurately evaluate the performance of the three models, PCA-BP and BP do the same training parameter settings as STEPDISC-PCA-BP model during model training. The PCA-BP model performed principal component analysis on the 12 factors, and the top six principal component scores were calculated and input to the BP neural network. The BP model directly input the 12 factors for training, so the number of input layer nodes was different for the three models. The observed results of the three models after training are shown in Figure 9.  
As can be seen from Figure 9, the STEPDISC-PCA-BP model with five input nodes converged to a training error of 0.03 after 372 iterations, took 0.333 min, and had the accuracy of 85.5%. In contrast, PCA-BP and BP models with the same neural network configuration and actual training times, with more input nodes and training times of 0.917 and 1 min, respectively, did not converge to the target minimum error, and the accuracy was only 61.77% and 56.69%. In a comprehensive comparison, the STEPDISC-PCA-BP model has lower data dimensionality, weaker factor correlation, simpler structure, faster convergence, and higher prediction accuracy, which is more suitable for application in local air quality prediction research and helps to improve the forecasting level.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Level** & **TP** & **FN** & **FP** & **TN** \\ \hline
1 & 74 & 32 & 16 & 409 \\
2 & 329 & 16 & 47 & 139 \\
3 & 33 & 19 & 5 & 474 \\
4 & 9 & 8 & 3 & 511 \\
5 & 6 & 1 & 5 & 519 \\
6 & 3 & 1 & 1 & 526 \\ \hline \hline \end{tabular}
\end{table}
Table 9: TP, FN, FP, and TN values of the STEPDISC-PCA-BP model.