esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In wireless body area sensor networks (WBASNs), Quality of Service (QoS) provision for patient monitoring systems in terms of time-critical deadlines, high throughput and energy efficiency is a challenging task. The periodic data from these systems generates a large number of small packets in a short time period which needs an efficient channel access mechanism. The IEEE 802.15.4 standard is recommended for low power devices and widely used for many wireless sensor networks applications. It provides a hybrid channel access mechanism at the Media Access Control (MAC) layer which plays a key role in overall successful transmission in WBASNs. There are many WBASN's MAC protocols that use this hybrid channel access mechanism in variety of sensor applications. However, these protocols are less efficient for patient monitoring systems where life critical data requires limited delay, high throughput and energy efficient communication simultaneously. To address these issues, this paper proposes a frame aggregation scheme by using the aggregated-MAC protocol data unit (A-MPDU) which works with the IEEE 802.15.4 MAC layer. To implement the scheme accurately, we develop a traffic patterns analysis mechanism to understand the requirements of the sensor nodes in patient monitoring systems, then model the channel access to find the performance gap on the basis of obtained requirements, finally propose the design based on the needs of patient monitoring systems. The mechanism is initially verified using numerical modelling and then simulation is conducted using NS2.29, Castalia 3.2 and OMNeT++. The proposed scheme provides the optimal performance considering the required QoS.  
patient monitoring systems; WBASN; QoS; IEEE 802.15.4; IEEE 802.15.6; frame aggregation; ECG; duty cycle; energy efficiency
# Multi-Layer QoE Learning System Implemented by Fiberhost
## 4 Compound QoE Metric Used in Multi-Layer QoE assessment  
In this section, we present Fiberhost's efforts to implement a system that monitors not only the QoE related to offered services, but also all interactions with the services and the personnel of the operator. This idea is already described in great detail in [23]. However, for reference, we also provide brief information here.  
The QoE metric used in Fiberhost is the end product of the multi-level QoE learning system described in the previous sections. We named the metric CX (Customer eXperience) and defined it as the sum of Digital eXperience (DX) and Analog eXperience (AX). DX is a component calculated from metrics collected from the network, so it is similar in nature to compound QoE metrics such as NPS [40]. Analog eXperience (AX) is designed to capture all human-operator interactions, not only with technical staff, but also with sales, finances, etc. An example of the distribution of CX values in one of the operational geographical areas is shown in the Figure 8. The current CX implementation uses predefined weights set for all parameters, as in the approach described in Section 3.2. However, in future work, we plan to use weights obtained from machine learning models, and these weights will be trained offline, for better accuracy and adaptability to the short and long-lasting changes occurring in the network.  
Figure 8: Example CX aggregation as calculated for a specific geographical area.  
Figure 7: MOS Distribution.  
The implementation of CX creates the foundation that can then be used by machine learning (ML) methods to identify more complex network dependencies and optimize service performance. However, ML-based optimization is planned for future work, while currently the practical use of CX is to improve the level of CX in all underperforming areas.
# HCLE: Hybrid Clustering Approach for Extending WBAN Lifetime
## 4 Materials and Methods
### Time Complexity  
We first analyze the time complexity of Algorithm 1 (i.e., the parent node selection process) and Algorithm 2 (i.e., data transmission), and then we combine the time complexities of both algorithms to obtain the overall time complexity for our proposed protocol.  
1. Parent node selection process: This algorithm aims to select appropriate parent nodes for transmitting the data from the two-hop sensor node to the selected parent node and then relaying it to the sink. This algorithm runs on the sink node and involves the following steps: 1. Calculating the cost function for all two-hop sensor nodes at the sink node. This step has a time complexity of \(O(N)\), where \(N\) represents the number of nodes in the network (lines 1 to 5 in Algorithm 1); 2. Sorting the nodes based on their cost function values. The sorting operation has a time complexity of \(O(N\log(N))\) (line 7 in Algorithm 1), using merge-sorting; 3. Storing the indices of the sorted nodes requires \(O(N)\) time complexity (lines 9 to 11 in Algorithm 1); 4. Iterating through the sorted nodes to select those serving a limited number of nodes and having sufficient energy for packet transmission. This process includes both static clustering (lines 12 to 22 in Algorithm 1) and dynamic clustering (lines 23 to 35 in Algorithm 1) parts. This step is applied to each two-hop sensor node. Thus, the time complexity of these iterations has a maximum of \(O(N^{2})\). By summing up the time complexities of these steps, the overall time complexity of the parent node selection algorithm is \(O(N)+O(N\log(N))+O(N)+O(N^{2})\), which can be simplified to \(O(N^{2})\).
2. Data transmission: This algorithm transmits data from the sensor node to the sink node. If a node sends a critical packet with sufficient energy, it transmits it directly to the sink (lines 3 to 6 in Algorithm 2). However, when a node sends a normal packet with enough energy, it sends the packet to its parent node, which the sink node chooses. The parent node then forwards the packet to the sink (lines 10 to 16 in Algorithm 2). The steps include: 1. Iterating through all \(N\) nodes in the network for varying rounds \(R\). This operation has a time complexity of \(O(RN)\). By combining the time complexities of both algorithms, the total time complexity of our proposed protocol is \(O(N^{2})+O(RN)\). Since the dominant term is \(O(N^{2})\), we can express the overall time complexity as \(O(N^{2})\). Regarding space complexity, the algorithm mainly requires memory for storing network information, such as node indices, locations, energy levels, RSSI, and any additional data structures used for sorting and calculations. The space complexity depends on the number of nodes and the size of the data structures, but it is typically within reasonable bounds for most WBAN applications.
# The Internet of Drones: Requirements, Taxonomy, Recent Advances, and Challenges of Research Trends
## 5 Commercial Case Studies
### Matternet  
Matternet is the leading technology company for independent urban drone logistics systems, offering the technology platform as a service for healthcare and logistics organizations for the transport of medical supplies through dispersed healthcare networks. Matternet has successfully deployed drone networks all over the world and has been licensed by the Swiss Federal Office of Civil Aviation (FOCA) and the United States for regulatory purposes. The Federal Aviation Administration (FAA) carries out fully autonomous operations beyond the line of sight and flights over people to carry essential, important medical supplies. To date, over 10,000 commercial revenue drone flights have been facilitated by Matternet technology [57; 58].  
In order for Matternet to operate drones in Zurich, local regulators required a flight permit. Drone reliability and safety were crucial regulatory requirements. Regulators required that human operators access the status and location of the drone in real-time and had the ability to manually interact with and monitor the drone, if necessary. Moreover, Matternet had to incorporate data on air traffic control in real-time since helicopters frequently landed near hospitals. The built-in Matternet solution includes self-employed drones and landing stations, as well as a logistics cloud platform. The operations platform is in charge of flight planning and drone surveillance in real-time. A flight director can track the flight status and send orders to drones to come home or wait at certain places [59].
# Seeing the Unseen: Real-Time Micro-Expression Recognition with Action Units and GPT-Based Reasoning
## 1 Introduction
While prior studies have demonstrated the effectiveness of machine learning and deep learning frameworks on micro-expression datasets, few have considered how LLMs--traditionally used in natural language processing--can be integrated into emotion recognition systems. Recent evidence suggests that these models, when contextualized with structured rules and domain knowledge, can enhance the emotion classification accuracy and reduce ambiguity, especially when emotions are embedded within complex affective states. The integration of LLMs with visual processing opens new possibilities for decoding not only facial movements, but also their underlying emotional narratives. This synergy between language and vision may ultimately support more nuanced interpretations of nonverbal communication. In doing so, it also addresses a limitation of conventional systems, which often rely on binary or simplistic emotion categories and fail to capture the complexity of emotional expression. The primary objectives of this research were as follows:  
O1: Develop a real-time system for micro-expression detection and classification, tested on the CASME II dataset;  
O2: Implement a hybrid methodology combining expert rules with machine learning and LLMs;  
O3: Resolve frequent emotional confusions using rule-based reasoning and signal enhancement;  
O4: Conduct a rigorous performance evaluation using standard accuracy and Fl-based metrics. This paper contributes to the scientific community by demonstrating that micro-expression recognition can be significantly improved through a synergistic integration of structured knowledge, visual computation, and large-scale generative AI models. It further lays the foundation for future systems that are capable of understanding nuanced human emotions in real-time and in diverse application settings. The remainder of this paper is structured as follows. Section 2 reviews the relevant literature in micro-expression recognition, focusing on hybrid methods and LLM integration. Section 3 presents the materials and methods including dataset preprocessing, system architecture, and the hybrid classification pipeline. Section 4 details the experimental design and validation protocols. Section 5 discusses the results, comparative evaluations, and real-time performance. Section 6 concludes the paper by highlighting the contributions, limitations, and directions for future research.
## 3 Related Work
### Federated Learning Against Poisoning Attacks  
Federated learning systems remain vulnerable to multiple poisoning attack variants, which researchers typically classify into two primary dimensions. From an objective perspective, attacks can be divided into non-targeted and targeted attacks. The former seeks to degrade model performance across all test data, while the latter selectively impairs recognition capabilities for specific inputs while maintaining normal prediction accuracy for other data. From another perspective (i.e., examining adversarial capabilities), attacks manifest as either data corruption or parameter manipulation. Malicious actors conducting data poisoning attacks tamper with local training datasets to indirectly influence parameter updates through contaminated samples. In contrast, model poisoning attacks involve direct manipulation of parameter updates on compromised devices through gradient alteration.  
To address poisoning threats from malicious participants, various Byzantine-resilient aggregation techniques have been developed for federated learning systems. For example, Krum [17] counters these attacks by identifying gradient vectors with minimal Euclidean distance to their majority neighbors. Trim-mean [19] adopts a ranking approach, excluding extreme model updates before calculating the trimmed average of remaining gradients. RLR [30] offers an efficient defense against back door attempts by dynamically adjusting the server's learning rate based on sign pattern analysis of client contributions. FLTrust [31] utilizes a curated reference set to generate baseline model updates, effectively neutralizingmalicious inputs without relying on client majority assumptions. PEFL [32] achieves dual protection against data poisoning and privacy breaches through homomorphic encryption-based malicious behavior identification in encrypted gradient space. While traditional FL security solutions focus on centralized architectures, blockchain-integrated federated learning (BCFL) enhances security during aggregation through the use of consensus-aligned update validation mechanisms [33].
## 3 Numerical Results
### Transient Responses of w(a/2,b/2) and \(\sigma_{x}\)  
The transient responses of amplitude \(w(a/2,b/2)\) (mm) and stress \(\sigma_{x}\) (GPa) at the center position of outer surface z = 0.5 \(h^{*}\) during \(t\) = 0.001-0.025s are presented in Figure 6 for \(L/h^{*}\) = 5, \(M_{\infty}\) = 2, and \(k_{c}c(t)\)= 0 under \(\theta_{L}\) = 60\({}^{\circ}\), respectively, with \(c_{1}\) = 0.925925/mm\({}^{2}\) and \(c_{1}\)= 0.0/mm\({}^{2}\), by fixing the values of \(\omega_{11}\) = 0.008380/s and \(\gamma\) = 15.707963/s. Also, it used \(L/R\) = 1, \(h^{*}\) = 1.2 mm, \(R_{n}\) = 2, \(T\) = 600 K, and \(\overline{T}_{1}\) = 100 K. Figure 6a shows the comparisons of advanced transient response \(w(a/2,b/2)\) under \(c_{1}\) = 0.925925/mm\({}^{2}\) and \(c_{1}\) = 0.0/mm\({}^{2}\). The absolute transient values of center displacements for \(c_{1}\) = 0.925925/mm\({}^{2}\) have greater values of firstly oscillating and then decreasing than that for \(c_{1}\) = 0.0/mm\({}^{2}\). Figure 6b shows the comparisons of advanced transient response \(\sigma_{x}\) under \(c_{1}\) = 0.925925/mm\({}^{2}\) and \(c_{1}\) = 0.0/mm\({}^{2}\). The absolute transient values of stresses are greater in an increasing straight line for \(c_{1}\) = 0.0/mm\({}^{2}\) than for \(c_{1}\) = 0.925925/mm\({}^{2}\).  
Figure 6: _Cont._Furthermore, the compared transient responses of \(w(a/2,b/2)\) (mm) and \(\sigma_{x}\) (GPa) in \(M_{\infty}~{}=~{}2\) are presented, respectively, in Figure 7 by using the controlled value \(k_{c}c(t)\) and uncontrolled value \(k_{c}c(t)\) = 0, respectively, during \(t\) = 0.001-0.025 s, \(c_{1}\) = 0.925925/mm\({}^{2}\) for the FGM plates/cylindrical shells of \(L/h^{*}\) = 5, \(R_{n}\) = 2, \(T\) = 600 K, and \(\overline{T}_{1}\) = 100 K under \(\theta_{L}~{}=~{}60^{\lx@math@degree}\). Figure 7a shows the compared transient response values of \(w(a/2,b/2)\) (mm). The \(w(a/2,b/2)\) values in \(k_{c}c(t)\) = 1 \(\times\) 10\({}^{9}\) case can be controlled into smaller than that in \(k_{c}c(t)\) = 0 uncontrolled case. Figure 7b shows the compared transient response values of stress \(\sigma_{x}\) (GPa). The stress \(\sigma_{x}\) in \(k_{c}c(t)\) = 1 \(\times\) 10\({}^{9}\) case are in positive tensional values and the different signs with that in \(k_{c}c(t)\) = 0 uncontrolled case are in negative compression values. The trend and comparison of the transient curve due to the algorithms in the control process can be described as follows. When an open-loop system is used, the \(w(a/2,b/2)\) vibrations with \(k_{c}c(t)\) = 0 uncontrolled case are actuated. When a close-loop system is used, the \(w(a/2,b/2)\) vibrations with \(k_{c}c(t)\) = 1 \(\times\) 10\({}^{9}\) controlled case are actuated by a sensor, control-law controller, and Terfenol-D layer to reduce the displacements for FGM plates and shells in supersonic airflow.  
Figure 7: _Cont._  
A graphical chart explaining the calculated results due to \(c_{1}\), \(k_{c}c(t)\), and \(M_{\infty}\) is shown in Figure 8.
# Real-Time Remote Sensing of the _Lobesia botrana_ Moth Using a Wireless Acoustic Detection Sensor
## 2 Materials and Methods
### Sensor  
In this section, the particular setup used in laboratory and field experiments will be presented. The sensor consisted of four main parts: a microphone, a processing unit with an acoustic filter (a _Lobesia botrana_ matched filter) for the moth detection, a network connection unit and a data server. A block diagram of the wireless sensor is presented in Figure 9. To attract the moth to the sensing unit, a pheromone was used.  
Figure 10 shows the pseudo-code implemented in the processing unit that allowed the explanation of the process of acquisition of the signal and the sending of the message. The sensor worked as follows: the microphone acquired the acoustic signal emitted by a flying moth flying near to the sensor. The received signal went to a processing unit (via a USB) with an acoustic filter that matched the acoustic signature of a flying _Lobesia botrana_,  
Figure 8: (a) Acoustic signal of the _Lobesia botrana_. (b) PSD of the filter response from the _Lobesia_ signal.  
Figure 6: (a) Acoustic signal of the container with 50 moths; (b) PSD of the container with 50 moths (blue) and reference (red).  
Figure 7: PSD of the wideband impulse response of the matched filter.  
which was obtained before. Finally, if the received acoustic signal matched the filter, the data were sent through a network connection to a data server where the data of the number of moths were counted and the time and sensor ID were stored and displayed.
## 2 Results
### RNA-Seq Library Construction and Analysis
Then, C16:0-ACP, C18:0-ACP, C18:1-ACP, C18:2-ACP and C18:3-ACP could be further catalyzed by acyl-ACP thioesterase A (FATA), acyl-ACP thioesterase B (FATB) and palmitoyl-CoA hydrolase (PCH), respectively [52], to produce free FAs (including C16:0, C18:0, C18:1, C18:2 and C18:3) [20; 53]. Three unigenes encoding the FATB enzyme were identified (Table S2, Figure 2). Among these free FAs, oleic acid (\(\omega\)-9 C18:1) and linoleic acid (\(\omega\)-6 C18:2) as precursor substances are involved in the hexanoate pathway of the cannabinoid synthesis pathway to synthesize the hexanoate in hemp [48; 54; 55; 56]. Figure 1: Proposed THCA and CBDA biosynthetic pathway in _C. sativa_. LOX: lipoxygenase; HPL: hydroperoxide lyase; AAE: acyl-activating enzyme; DXS: 1-deoxy-D-xylulose-5-phosphate synthase; DXR: 1-deoxy-D-xylulose 5-phosphate reductaseoisomerase; MCT: 2-C-methyl-d-erythhitol 4-phosphate cytidylyltransferase; CMK: 4-diphosphocytidyl-2-C-methyl-d-erythhitol kinase; MDS: 2-C-methyl-d-erythhitol 2,4-cyclodextrin diphosphate synthase; HDS: 4-hydroxy-3-methylbut-2-en-1-yl diphosphate synthase; HDR: 4-hydroxy-3-methylbut-2-enyl diphosphate reductase; IPPI: Isopentenyl diphosphate isomerase; GPPS: Geranyl diphosphate synthase; OLS: olivetol synthase; PT: ppenyltransferase; THCAS: tetrahydrocannabinolic acid synthase; CBDAS: cannabidiolic acid synthase. In addition, 331 transcripts potentially involved in the cellulose metabolism were also identified. Cellulose synthase (CESA) can synthesize cellulose at the plasma membrane in land plants [57], which contain 27 transcripts. Sucrose synthase (SuSy) catalyzes UDP into UDP-glucose for each CESA can synthesize a single glucan chain [58], which contains 18 transcripts. The UDP-galactose and UDP-glucose transporter are reported that make substrates available for polysaccharide biosynthesis and have an important impact on maintaining cell wall integrity [59; 60]. There were seven and 28 transcripts classified into UDP-galactose and UDP-glucose transporters, respectively. In the process of secretory vesicles targeting the SCW domain, the motor proteins kinesin-4 and kinesin-like proteins are involved in cell transport in microtubules [61; 62], which contained four and 175 transcripts. There was a number of non-CESA proteins also involved in cellulose production, including COBRA-like protein (COBL), endo-1,4-beta-glucanase, chitinase-like protein (CTL), alpha-glucosidase I, VILLIN, katanin-like protein, glycosyltransferase-like protein KOBITO1 and inositol polyphosphate 5-phosphatase (FRA) [63; 64; 65; 66; 67; 68; 69; 70; 71; 72], which contained eight, one, four, one, two, three, one and three transcripts, respectively (Table S3).
###### Abstract
Even though different data rates are employed in SM-LDM systems, Bi-PA enhances bit error rate (BER) fairness across layers. The ideal power ratios are adaptively determined by balancing signal-to-interference plus noise ratios with a preference for the lower layer (\(LL\)) that involves a higher modulation order. SAS alleviates the complexity of successive interference cancellation and enhances spectral and energy efficiencies. Both the \(LL\) and upper layer (\(UL\)) share the antenna selection decision and transmit using a single antenna. The \(UL\) carries a space shift keying signal while the entire power is allocated for the \(LL\). We analyze the spectral efficiency for the SAS-based SM-LDM system with finite alphabet inputs. Numerical results demonstrate the advantages of the proposed approaches. Compared to pre-assigned-PA (Pre-PA), Bi-PA shows nearly identical BERs for both layers and solves the error floor problem. The sharing property and common layer transmission of SAS-based SM-LDM yield a significant BER reduction relative to conventional SM-LDM. It provides gains ranging from 7 to 15 dB for \(LL\) at BER equal to \(10^{-3}\), while \(UL\) performance ranges from slight gain to minor loss. Furthermore, both Bi-PA and SAS techniques enhance the achievable \(LL\) rate and sum-rate at low and intermediate signal-to-noise ratio values. They can achieve an improvement of up to two bits in \(LL\) rate and less than one bit in sum-rate at a signal-to-noise ratio of \(-0.5\) dB. These findings show that both proposed techniques have a considerable impact on enhancing the fairness, BER performance, and feasible rates of SM-LDM systems, making them promise for broadcast system designs. Footnote †: journal:  
2023
# Energy-Efficient Region Shift Scheme to Support Mobile Sink Group in Wireless Sensor Networks
## 5 Conclusions  
In this paper, we propose a novel scheme for supporting mobile sink groups in wireless sensor networks. Our scheme avoids location updates of individual member sinks to a source in multicasting scheme, GMR. Different from M-Geocasting, since our scheme takes into account the inherent property of mobile sink group in which the movement of only sinks that deviate from the current sink group  
Figure 12: The number of available nodes according to elapsed simulation time.  
Figure 13: Control overhead impacted by guard area size.  
region reflects the movement of the mobile sink group, it calculates efficiently a group region with the location information of only the deviated member sinks instead of the location information of all member sinks. Thus, our scheme saves much energy through both low signaling overheads for calculating region of mobile sinks group. Simulation results show that our scheme consumes less energy than GMR and M-Geocasting.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (Grant No. NRF-2015R1D1A3A01019680). In addition, this work was supported by Zayed University Research Office, Research Cluster Award #17079. [PERSON] and [PERSON] contributed to protocol design and the detailed algorithm. [PERSON] and [PERSON] contributed to the performance evaluation and analysis.  
The authors declare no conflict of interest.