esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Tool wear modeling in CNC turning processes is critical for proactive maintenance and process optimization in intelligent manufacturing. However, traditional physics-based models lack adaptability, while machine learning approaches are often limited by poor interpretability. This study develops Kolmogorov-Arnold Networks (KANs) to address the trade-off between accuracy and interpretability in lathe tool wear modeling. Three KAN variants (KAN-A, KAN-B, and KAN-C) with varying complexities are proposed, using feed rate, depth of cut, and cutting speed as input variables to model flank wear. The proposed KAN-based framework generates interpretable mathematical expressions for tool wear, enabling transparent decision-making. To evaluate the performance of KANs, this research systematically compares prediction errors, topological evolutions, and mathematical interpretations of derived symbolic formulas. For benchmarking purposes, MLP-A, MLP-B, and MLP-C models are developed based on the architectures of their KAN counterparts. A comparative analysis between KAN and MLP frameworks is conducted to assess differences in modeling performance, with particular focus on the impact of network depth, width, and parameter configurations. Theoretical analyses, grounded in the Kolmogorov-Arnold representation theorem and Cybenko's theorem, explain KANs' ability to approximate complex functions with fewer nodes. The experimental results demonstrate that KANs exhibit two key advantages: (1) superior accuracy with fewer parameters compared to traditional MLPs, and (2) the ability to generate white-box mathematical expressions. Thus, this work bridges the gap between empirical models and black-box machine learning in manufacturing applications. KANs uniquely combine the adaptability of data-driven methods with the interpretability of physics-based models, offering actionable insights for researchers and practitioners.  
Kolmogorov-Arnold Networks (KANs); tool wear modeling; interpretable machine learning; CNC turning processes; white-box mathematical modeling +
Footnote †: journal: Applied sciences
# Incorporating Artificial Intelligence Technology in Smart Greenhouses: Current State of the Art
## 3 Digital AI Models for Energy Management in Greenhouses
### Alternative Energy Saving Measures--LoRa Transmission, PCOA-Lighting Management, PCMs, and LEDs
Even in regions where the supply of solar energy was guaranteed, mass utilization of renewable energy resources was a challenge, owing to the significant disconnect between the use of AI in the greenhouse and the deployment of AI in energy saving; this was a paradoxical phenomenon, considering smart greenhouses (featuring predictive light control systems) could reduce energy expenditure by 14-33.85% [11]. The improvements in lighting achieved by [11] were in tandem with [58], who documented a 19.4% cost saving following the introduction of a predictive control strategy and innovative greenhouse lighting system. The light prediction model is illustrated in Figure 8.  
Following the comparison of the heuristic versus application-specific models, the former yielded accurate predictions with minimal variations from the baseline. Beyond the lower prediction accuracy, the case against the heuristic systems was further corroborated by the high cost of heuristic-based predictions compared to the prediction-based method (PBM). On average, the heuristic-based predictions cost $5.96/m\({}^{2}\), while the PBM cost $0.73/m\({}^{2}\) per day [11]. The notable disparities in the cost validate the PBM approaches. Proper energy management in greenhouse structures remained a priority, considering that the current rates of energy expenditure were unsustainable in the long term. For example, US-based greenhouse operators utilize $600 million worth of electricity per year [160]. Regulating energy use using AI was a practical option, since small-scale and large commercial farms had to spend additional resources on IPM. On average, US farmers spend $1 billion on pesticides per year. Similarly, commercial farming in Europe was pesticide intensive--farmers utilized an estimated 346,000 tons of pesticides in 2020 alone [120]; nearly half of the expenses were on fungicides and bactericides.
# Evaluation of Survivability of the Automatically Obfuscated Android Malware
## 2 Materials and Methods
### Dynamic Analysis  
#### 2.4.1 Automatic Dynamic Analysis  
For the dynamic analysis using VirusTotal API [25], the original and obfuscated APKs were submitted to VirusTotal and results were retrieved thereafter. The results were based on the execution behavior analyzed by any two of the Android Sandboxes, namely R2 DBox and Droidy, used by VirusTotal. The process of submitting and retrieving results was performed with the help of custom Python scripts to facilitate a large number of sample  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Permission Name** & **Permission Name** \\ \hline ACCESS\_BACKGROUND\_LOCATION [Added: API level 1] & USE\_SIP [Added: API level 9] \\ \hline ACCESS\_COARSE\_LOCATION [Added: API level 1] & MODIFY\_PHONE\_STATE [Added: API level 1] \\ \hline ACCESS\_FINE\_LOCATION [Added: API level 1] & WRITE\_CALENDAR [Added: API level 1] \\ \hline CALL\_PHONE [Added: API level 1] & INSTALL\_PACKAGES [Added: API level 1] \\ \hline READ\_PHONE\_STATE [Added: API level 1] & WRITE\_CONTACTS [Added: API level 1] \\ \hline READ\_SMS [Added: API level 1] & READ\_CALENDAR [Added: API level 1] \\ \hline RECEIVE\_MMS [Added: API level 1] & GET\_ACCOUNTS [Added: API level 1] \\ \hline RECEIVE\_SMS [Added: API level 1] & READ CONTACTS [Added: API level 1] \\ \hline RECEIVE\_WAP\_PUSH [Added: API level 1] & READ\_CALL\_LOG [Added: API level 16] \\ \hline READ\_EXTERNAL\_STORAGE [Added: API level 16] & WRITE\_APN\_SETTINGS [Added: API level 1] \\ \hline ACCESS\_MEDIA\_LOCATION [Added: API level 29] & RECORD\_AUDIO [Added: API level 1] \\ \hline ACTIVITY\_RECOGNITION [Added: API level 29] & CAMERA [Added: API level 1] \\ \hline ANSWER\_PHONE\_CALLS [Added: API level 26] & SEND\_SMS [Added: API level 1] \\ \hline BODY\_SENSORS [Added: API level 20] & WRITE\_CALL\_LOG [Added: API level 16] \\ \hline READ\_PHONE\_NUMBERS [Added: API level 26] & PROCESS\_OUTGOING\_CALLS [Added: API level 1, Deprecated: API level 29] \\ \hline \hline \end{tabular}
\end{table}
Table 6: Dangerous permissions.  
submissions and analyses. The analysis achieved the following two goals: (1) estimating the detection ratio for the obfuscated malware and (2) evaluating the installability/\"survival\" ratio of the obfuscated software as automated obfuscation may potentially damage the functionality of the applications. Of course, automated checkups by sandboxes may provide false-positive and false-negative results for the installability of the obfuscated software. To address this potential issue, random sample sets of the obfuscated applications were subjected to manual dynamic verification.
# Semantics-Preserving RDB2 RDF Data Transformation Using Hierarchical Direct Mapping
## 3 Preliminaries  
### Direct Mapping  
Developed in 2010 and recommended in 2012 by the W3C RDB2 RDF Working Group, direct mapping is an automatic map-creation method that transforms relational input data, including schema data, into RDF graph data (i.e., a direct graph). Direct mapping can be viewed as a function of transforming relational data with integrity constraints to semantic data. Figure 4 provides an example of relational input data via the direct-mapping process. The table, \"Product,\" contains an  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Method** & **Authors** \\ \hline \multirow{6}{*}{\
\begin{tabular}{c} Manual \\ RDB2 RDF \\ (Domain semantics-driven mapping) \\ \end{tabular} } & R2 RML & Hert et al. (2011) \\  & & RDB2 RDF Working Group (2012) \\ \cline{2-3}  & & D2 RQ & Bizer et al. (2004, 2009) \\ \cline{2-3}  & & [PERSON] (2007) \\ \cline{2-3}  & & [PERSON] et al. (2009, 2013) \\ \hline \multirow{6}{*}{
\begin{tabular}{c} Automatic \\ RDB2 RDF \\ (Direct mapping) \\ \end{tabular} } & & [PERSON] et al. (2012) \\  & & [PERSON] et al. (2004) \\ \cline{2-3}  & Augmented mapping & [PERSON] et al. (2007) \\ \cline{2-3}  & using OWL & Li et al. (2005) \\ \cline{2-3}  & & [PERSON] et al. (2015) \\ \cline{2-3}  & & [PERSON] et al. (2008) \\ \cline{2-3}  & [PERSON] et al. (2020) \\ \cline{2-3}  & DB2 OWL & [PERSON] et al. (2007) \\ \cline{2-3}  & RDBToOnto & Cerbah (2008) \\ \cline{2-3}  & OWLED & Shen (2006) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification of previously reported mapping methods.  
attribute, pld, as its primary key, an attribute name, and an attribute production as a foreign key that references a table called \"Production.\" Production contains an attribute, pCd, as its primary key and an attribute name. Figure 5 presents the result of the direct-mapping process regarding the input data shown in Figure 4. The output graph comprises a set of RDF triples. Suppose that the base URI of the output data is <[[http://idb.snu.ac.kr/example/](http://idb.snu.ac.kr/example/)]([http://idb.snu.ac.kr/example/](http://idb.snu.ac.kr/example/))>. The primary key attributes with the base URI are used to generate the subject resource. Two _Product_ resources and two _Production_ resources are generated. Predicates are generated from the attribute names of relational tables, and objects are generated from the attribute values.
# Aiding ICD-10 Encoding of Clinical Health Records Using Improved Text Cosine Similarity and PLM-ICD
## 4 Methodology
### Cosine Text Similarity Method  
The Cosine similarity method measures the similarity between two vectors of an inner product space. It is measured by the Cosine of the angle between these two vectors, as given by Equation (4), where \(a\) and \(b\) are two vectors and \(\theta\) the angle they form.  
\[cos(\theta)=\frac{a.b}{||a||.||b||} \tag{4}\]The method can be used to compare documents, as long as the documents are represented as vectors. A result of 0 means the two vectors are at 90 degrees and the documents they represent have no match. A result of 1 means the vectors representing the texts totally overlap, so the cosine is 1 and the documents are very similar. The closer to 1, the greater the overlap of the vectors and the greater the match between texts.  
In order to encode the texts as vectors, one method that is popular and was used in the present work is the TF-IDF. TF-IDF representation tells how unique a word is across multiple text. It is computed by summing the TF-IDF for each query term and is given by the Equations (5)-(7).  
\[TF=\frac{NW}{TW} \tag{5}\]  
\[IDF=\log\left(\frac{TD}{ND}\right) \tag{6}\]  
\[TF-IDF=TF\times IDF \tag{7}\]  
In the equations, \(NW\) is the number of times the word appears in the document. \(TW\) is the total number of words in the document. \(TD\) is the number of documents in the corpus, and \(ND\) is the number of documents in the corpus that contain the term.
## 3 System Requirements for BVLOS Flight Operations
### First Person View (FPV) System  
The RAVEN FPV system is used as an additional safety feature in BVLOS flight operations and is mandated by the RAVEN SFOC for use/installation on UA involved in BVLOS operations [12]. The FPV system provides situational awareness to the Pilot-in-Command (PIC) and increases the pilot's ability to recover the UA from unexpected behaviour, or allows the PIC to control the UA to avoid potential risks. In addition to the FPV video, the system provides important telemetry data to the PIC and other ground operators including location, altitude, speed, attitude, heading, direction to home, power consumption, and other user selectable parameters.  
The FPV system uses an independent set of sensors for telemetry and does not depend on any sensors used by the autopilot. In Figure 6a, the FPV camera has pan and tilt capabilities when operated within the R/C C2 link range (about 2 km). To increase the reliability of the FPV system's reception, the video receiver in Figure 6b has two antennas installed, i.e., a low-gain omnidirectional antenna is used to receive signals when UA are close to the GCS and a high-gain directional antenna is used when UA are further from the GCS.  
Figure 5: Block diagram for flight termination system.  
Figure 6: Block diagrams for the FPV system. **(a)** FPV airborne side; **(b)** FPV ground side.
## 4 Simulation and Analysis  
Taking the IEEE 39-bus New England system as an example, it is assumed that the system is in a heavy-load operation state. Based on the PSASP platform, the simulation analysis is carried out to verify the effectiveness of the CLS search method proposed in this paper and the accuracy and adaptability of the LE identification method under different penetrations of wind power generation.
# Improving IMES Localization Accuracy by Integrating Dead Reckoning Information
## 3 IMES Radio Propagation Model  
A radio propagation model is an empirical mathematical model that is used to describe the characteristics of radio wave propagation with respect to frequency, distance from the source and other conditions. In practice, developing a precise radio propagation model is a difficult and complex process owing to the dynamic and unpredictable nature of the environment. However, the radio propagation model remains the most important model for RSS-based indoor positioning, because it is the most straightforward way to describe the relationship between the RSS and the distance from the transmitter.  
To use IMES radio for high accuracy indoor positioning, we need to develop a practical IMES radio propagation model through experiments. In an experiment, an IMES radio transmitter was set at a height of about 2.2 m within an empty indoor environment for broadcasting signals, and an IMES radio receiver was mounted on a mobile robot. The receiver receives the IMES radio signal, as well as its carrier noise ratio (CNR) at different locations within the indoor environment. In this study, the CNR represents the RSS of the IMES radio, and these two expressions are equivalent.  
In our experiment, radio signals were received at over 40,000 points within a 4 m \(\times\) 4 m area with the IMES transmitter at its center. The distance of each point from the IMES transmitter was calculated. The relationship between the distance and its corresponding CNR measurement is plotted in Figure 1.  
Figure 1: Indoor Messaging System (IMES) radio propagation model.  
The empirical signal path loss model was used to model the relationship between the CNR of the IMES radio and the distance as follows:  
\[cntr =-10\times\gamma\times log_{10}\sqrt{d^{2}+h^{2}}+\alpha\] \[d =\sqrt{x^{2}+y^{2}} \tag{1}\]  
where \(cntr\) is the received CNR of the IMES radio. As shown in Equation (1), the CNR decreases with the 3D distance between the receiver and the transmitter: \(\sqrt{d^{2}+h^{2}}\). The 3D distance is calculated based on the \(x,y\) coordinates of the receiver in the horizontal plane and the height \(h\) of the transmitter. The IMES radio transmitter is set at a height of \(h=2196\) mm in the following experiments; \(\gamma\) is the path loss exponent and \(\alpha\) the constant path loss.  
Based on the experimental data plotted in Figure 1, model fitting was performed to obtain the parameters \(\{\gamma,\alpha\}\) of the path loss model; the results are obtained as \(\gamma=32.57,\alpha=151.46\). Thus, we obtained the IMES radio propagation model as follows:  
\[cntr=151.46-32.57\times log_{10}\sqrt{d^{2}+2196^{2}} \tag{2}\]  
This model is used in our indoor positioning method described in the following sections.
## 2 Related Work
### Adaptive Attention  
The adaptive attention mechanism enhances the performance of multi-modal tasks (e.g., image caption generation and speech enhancement) by dynamically adjusting attention weights. This mechanism is particularly effective in selectively emphasizing key information and enabling multi-level feature fusion. [PERSON] et al. [27] proposed a new encoder-decoder architecture that incorporated an adaptive attention mechanism to connect two LSTMS for image caption generation. One LSTM captured fine-grained visual representations and updated the adaptive attention, while the other LSTM generated words at each time step. This approach eliminated the interference between the visual and language modules during the LSTM description process. [PERSON] et al. [23] introduced a frequency dimension adaptive attention (FAA) mechanism to improve temporal convolutional networks (TCNs). This mechanism guided the model to selectively emphasize frequency features that contained important speech information, thereby enhancing the model's representation capacity. [PERSON] et al. [28] proposed a speech enhancement model that utilized an adaptive attention mechanism. The model integrated both local and global attention modules between the convolutional encoder and decoder. The local attention module (LAM) combined channel and spatial attention, allowing the model to focus more on local features within speech blocks, especially frame-level features. Meanwhile, the global attention module (GAM), through a self-attention mechanism, focused on discourse-level features. [PERSON] et al. [29] introduced an adaptive selection network designed to dynamically choose the most appropriate attention mechanism for a given task. Based on this network, they developed the attention-selective speech enhancement network (AS-SENet). This model integrates both local and non-local attention mechanisms, employing dynamic selection techniques to explore optimal paths for applying attention mechanisms in speech enhancement tasks.
# A Comparative Study of 3D UE Positioning in 5G New Radio with a Single Station
## 2 System Hypotheses and Signal Modeling  
### Hypotheses  
In this paper, the main scope is to investigate the performance of 3D UE positioning rather than self-localization or navigation. The considered system sketch can seen in Figure 1a. To shed light on the positioning methods and performance metrics in the later sections, we list below the hypotheses and constraints at the beginning of the discussion:  
* Frequency band: This work focuses on the sub-6 GHz band (e.g., 3.5 GHz) of the 5G NR carrier bands. Compared with the mmWave band, the sub-6 GHz signal has less propagation loss and larger outdoor coverage, which is more suitable for manned or unmanned drones or vehicles.
* Receiving antenna: In order to obtain the 3D position of a signal source without trilateration or hyperbolic positioning, the positioning station (i.e., the base station) needs to be able to measure the azimuth, elevation angles, and the time delay simultaneously. Therefore, a uniform rectangular array (URA) is utilized at the receiver end to be capable of spanning the whole azimuth and elevation dimension. The modeling of URA is introduced in Section 2.3.
* Signal: The uplink 5G NR sounding reference signal (NR-SRS) sent by the UE is applied. The NR-SRS is an OFDM modulated Zadoff-Chu sequence that is feasible for time delay estimation. The introduction and modeling of SRS are given in Section 2.2.
* Algorithm: The extended subspace method and expectation-maximization (EM)-based algorithms are investigated for 3D positioning. The positioning algorithms are given in Section 3.
* Positioning host: The URA and positioning algorithms are hosted at the 5G base station (called gNB in 5G terminology), so as to leverage the computing power and energy supply to accommodate the large-scale URA in the sub-6 GHz band and run the positioning algorithms.  
Figure 1: (**a**) System schematic diagram of the URA-based 5G NR 3D positioning; (**b**) URA geometry and dimension illustration. SRS, sounding reference signal.
## 2 Integrating Precision Agriculture and IPv6 Low-Power and Lossy Networks
Meanwhile, the data center server can generate Point-to-Multipoint (P2 MP) communication through LBRs to configure A-LLN devices or initiate queries, and use unicast and multicast to efficiently communicate with a single A-LLN sensor node or LC node, or groups of these devices. Additionally, based on the LLN concept defined by ROLL WG, a layered architecture protocol stack installed in the A-LLN devices is friendly for any data link layer, such as the 802.15.4 and 802.11 families. In other words, periodic sensor monitoring dominates the traffic generated by A-LLN nodes, and PA applications normally do not have hard real-time constraints as the exchange of sensory data is slow and smooth in most of the time. Furthermore, A-LLN sensor nodes are always configured to run in Sleep&Wakeup mode, and they are often subject to bounded latency and stringent reliability service level requirements. From the perspective of routing requirements, A-LLN requires both of efficient MP2P and P2 MP communications. Besides, the routing protocol operating in A-LLN deployment needs to provide good scalability with network size and number of forwarding hops. More supplementary mechanisms, such as timely loop detection and resolution, broken link repair and QoS-aware routing path selection and optimization are still important due to the highly dynamic environment for A-LLN. Last but not least, A-LLN scenario can be regarded as a complement to the application-specific routing requirements defined and analyzed by IETF ROLL WG, such as urban, industrial, home automation, and building automation LLNs. Advanced metering infrastructure (AMI) (or smart grid) documented and stated more exhaustively in [22, 23] has greatly inspired our work. Therefore, the work of this article is also a further extension of our previous work [20].