esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Comparison of Simulated Muscle Force Curves with Measured Muscle Activations  
The courses of the simulated muscle forces are now compared with the muscle activations measured by sEMG for the selected muscles, as the main focus of the co-simulation model right now is the reproduction of the real occurrences into the virtual model. Figure 9 shows the comparison of the simulation results to captured sEMG activation results for all three subjects. Looking at the M. biceps brachii, the curves of the muscle forces roughly match the captured muscle activation. For subject 1, the course of the measured muscle activation also shows a similar activation for all support levels before the screwing process. During the screwing process, the force or activation decreases with no support and 50% support level. The increase in muscle force during the screwing process at the full support level of the exoskeleton is also shown by an increase in the measured muscle activation, but not with that excessive peak. For subject 2, the complete reduction in the screwing phase is not really represented by the measured sEMG data. However, a muscle reduction for the 100% support level can be seen; the 50% support level measured muscle activation stands out in negative way. The unusual rises of forces in M. biceps brachii force at 50% and 100% support level for subject 3 are also represented in the captured sEMG data.  
The M. deltoideus shows overall a pretty good agreement of the simulated muscle forces with the measured muscle activations. For subject 1, the reduce in force of the 100% support level is also existing in the sEMG captured data, but not to such an extent. For subject 3 at no support level, a muscle activation was recorded at the beginning of the movement and not during the screwing process.  
A comparison of the muscle force curves of the M. latissimus with the measured muscle activation shows good agreement, as the curves of all subjects show. It can also be seen that both the simulated muscle forces and the activation are very low. The small increase during the screwing process is present in both, the simulated muscle forces and the captured muscle activations.  
The curves for the M. flexor carpi ulnaris show that an increase in force and activation occurs both before and during the screwing process. In the simulation, however, the increase before the screwing phase is lower than during the screwing process. The increases in the measured activations appear to be similar at both times. It can also be seen that the differences between the various support levels are not large, both in the simulation and in the measurements. However, the results between the simulation and the measurement differ slightly. While the support level of 50% causes the lowest activation in the screwing process in the measurement data, the simulation shows the highest muscle forces here.
## 3 Results  
### Demographic Characteristics and Current Status  
Of the 13 teachers who participated in this study, 61.5% were females and 38.5% were males. Moreover, about 75% worked in public schools, while 25% were from a private school. The majority of the teachers (46%) were between 41 and 50 years old, followed by those older than 50 years (24%). Age ranges of 21-30 and 31-40 years accounted for 15% each. Regarding the overall teaching experience, 23% had between 1 and 10 years of experience, 39% had 11-20 years, 23% had 21-30 years, and 15% had more than 30 years of experience. Furthermore, 31% of the teachers had worked for over 10 years in the current school, while the others had worked for less. Additionally, about 39% had a bachelor's  
Figure 2: Teachers observing as the NAO robot tutors a pupil.  
degree, 15% had an ordinary diploma, and 46% had an ordinary certificate as their highest academic qualification.  
The teachers were also required to indicate the average number of pupils in their classes, where public schools had an average of 92 pupils per class, while the private school had an average of 35 pupils. Moreover, all the teachers in the private school taught no more than 2 subjects, while 70% of those in public schools taught 3-5 subjects, with only 30% teaching 2 subjects or less. In the same vein, all the teachers in the private school taught in two grades, while 23% of those in public schools taught in three grades or more.  
We also assessed the teachers' readiness to adopt various technologies for teaching and learning. All the teachers owned smartphones, with 77% also having laptops. Moreover, 61.5% confirmed having used technology for teaching and learning, while the rest never had. When prompted to elaborate on the technologies that they have used, some mentioned Microsoft Word and Excel to prepare teaching notes and reports and their smartphones to access the Internet and search for teaching materials and exercises. Online libraries, specifically the Tanzania Institute of Education (TIE) library, were identified as helpful in offering teaching materials. Lastly, we assessed their awareness of the concepts of AI and social robots. Only 38.5% were aware of AI, and 53.9% were aware of social robots.  
For the purpose of this study, it is important to note that 69.2% of the teachers had an actual interaction with the NAO robot and observed as it taught pupils, while 30.8% only saw videos. We will differentiate the data for the topics where live presence appears determinant.
# Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence
## 5 Utilization of AI Analysis in the IoMT
### Model Selection and Training
#### 5.4.2 Role of Segmentation Models

Table 11: Contemporary literature on the application of instance segmentation in healthcare. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Model Name** & **Brief Descriptions** & **Aspects to Address** \\ \hline \multirow{6}{*}{FAM-U-Net [76]} & Integrated Multiscale Feature Extraction (MFE) module and Deep Aggregation Pyramid Pooling Module (DAPPM) to extract the most pertinent features for fluid detection from Optical coherence tomography (OCT) images. Additionally, Convolution Block Attention Module (CBAM) is utilized in the skip connection paths of UNet to enhance the segmentation. & The model struggled to separate small targets with unclear boundaries, reducing segmentation accuracy, especially when dealing with overlapping aggregations. Also, the denoising process often removed small objects, impacting segmentation quality. The multi-step denoising approach and the diffusion model resulted in slow inference times \\ \hline \multirow{2}{*}{CoarseInst [104]} & Weakly supervised framework with box annotations includes coarse mask generation, self-training for instance segmentation, and lightweight encoder with cascade attention block for improved feature information & The model struggled to separate small targets with unclear boundaries, reducing segmentation accuracy, especially when dealing with overlapping aggregations. Also, the denoising process often removed small objects, impacting segmentation quality. The multi-step denoising approach and the diffusion model resulted in slow inference times \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Contemporary literature on the application of semantic segmentation in healthcare. Numerous advancements have been made in the field of instance segmentation, where the goal is to not only classify objects but to delineate individual instances within an image. Models such as DeepMask [134], SharpMask [134], and Multipath Network [134] have pioneered the use of DL for instance segmentation, leveraging techniques such as fully convolutional networks (FCNs) and instance-specific feature learning. Additionally, Instance FCN introduces a specialized architecture for instance segmentation tasks, further improving accuracy and efficiency [134]. More recent models such as FCIS [123], Mask RCNN [123], and MaskLab [123] have refined instance segmentation through innovations such as fully convolutional instance segmentation (FCIS) and region-based methods such as Mask RCNN, which combine object detection with instance segmentation for improved
# Energy-Efficient Check-and-Spray Geocast Routing Protocol for Opportunistic Networks
## 7 Conclusions and Future Works  
We have proposed a novel geocasting routing protocol for OppNets called EECSG, in which an effective way of message distribution in the geocasting region to all residing nodes is introduced, which has led to energy consumption savings by restricting the unnecessary packet transmission in that region. Simulations have shown that our proposed EECSG protocol outperforms the Efficient and GSAF and the CGOPP routing protocols with respect to delivery ratio, average latency, number of dead nodes, hop count, and overhead ratio. In future, we plan to design a security-aware version of the proposed EECSG scheme against network attacks such as wormhole, sybil, and blackhole attacks.  
design of the protocol, K.K. and J.S.; methodology, I.W., S.K.D., and J.J.P.C.R.; algorithm implementation, K.K. and J.S.; simulations and validation, K.K.; analysis, I.W. and J.J.P.C.R.; writing--review and editing, all the authors. All authors have read and agreed to the published version of the manuscript.  
This work is supported in part by a grant from the National Science and Engineering Research Council of Canada (NSERC), REF# : RGPIN 2017 04423, held by the second author. It is also partially funded by FCT/MCTES through national funds and when applicable co-funded EU funds under the Project UIDB/50008/2020; and by Brazilian National Council for Scientific and Technological Development--CNPq--via Grant No. 309335/2017-5, held by the 5 th author.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### System Architecture
#### 2.1.1 Modular Layered Design
During ingestion each resource is (i) assigned a globally unique, resolvable identifier; (ii) enriched with machine-readable metadata using the HL7 FHIR v5.0.0 information model; (iii) linked to controlled vocabularies (SNOMED CT [56], LOINC [57], RxNorm [58]) through the OMIS. Figure 3 summarizes the steps and shows how they integrate with the GDPR controls already described. The result is data that are _Findable_ through FHIR endpoints and DOI-minted bundles, _Accessible_ under OAuth2 scopes, _Interoperable_ via semantic mappings, and _Reusable_ thanks to explicit license and provenance tags. The implementation of FAIR data-stewardship principles within the proposed CDSS is detailed in Table 2.  
To move from high-level intent to verifiable compliance with GDPR, the CDSS introduces a four-pillar framework (detailed in Supplementary Table S1) that maps each architectural layer to specific legal requirements. Beyond static mapping, the system enforces dynamic compliance through a dedicated rights-management engine and continuous monitoring components, described below. **Data-Subject Rights Workflow**  
A lightweight \"rights engine\" exposes REST endpoints/gdpr/access, /gdpr/rectify, /gdpr/erase, and/gdpr/restrict. When a right-to-erasure request is validated, the engine:  
1. Locates the master pseudonym of the patient. 2. Sends tombstone events that cascade through Kafka to trigger deletion in the big-data lake, revocation of FL gradients, and cache invalidation in the LLM embedding store. 3. Issues signed confirmation to the requester and updates Article 30 of the record of processing activities. **Continuous Compliance Monitoring**  
Prometheus scrapes custom exporters that emit the following:  
* gdpr_erasure_latency_seconds--time from request to final purge (objective \(\leq\) 72 h). * fl_dp_epsilon_current--latest global \(\varepsilon\) reported by the FL aggregator. * phi_fields_in_logs_total--Grafana alert if \(>\)0. Monthly compliance dashboards surface these KPIs to the Data-Protection Officer and hospital IT leadership, complementing the existing uptime and model-performance metrics already described.
# Radiation-Hardened 16T SRAM Cell with Improved Read and Write Stability for Space Applications
## 3 Simulation Results and Analysis  
This section aims to evaluate the characteristics of the RHHS16T cell through simulations using 90 nm CMOS technology. To achieve high read stability and appropriate read speed, the widths of transistors N5 and N6 in the proposed cell are set to 2.0 times that of access transistors N1 and N9, while the widths of transistors N4 and N8 are set to 3.3 times that of access transistors N2 and N10. Moreover, to achieve high write stability and short write access time, the widths of transistors P2 and P4 in the proposed cell are set to 0.5 times that of the respective access transistors, and the widths of transistors P1 and P6 are set to 0.66 times that of the respective access transistors. This paper presents the measurement results of read delay, write delay, RSNM, WWTV, and hold power for the proposed RHHS16T cell compared with WE-QUATRO, QUCCE12T, RHBD10T, RHD12T, RSP14T, RHBD14T, and RHPD14T, conducted using the ratios presented in the respective papers. For RHD12T, which does not specify transistor sizes in the paper, appropriate sizes were used for measurement.
# Optimization Technique for High-Gain CMOS Power Amplifier for 5G Applications
## 2 Design and Optimization of the Power Amplifier
### Configuration of Matching Network with Transformers  
Figure 8 shows a schematic of the designed PA, which comprises a three-stage configuration, each stage consisting of a common source structure. The matching process at each stage is shown in Figure 9. The smith charts shown in Figure 9 were normalized to 50 \(\Omega\). Figure 9a shows the input and interstages matched with the conjugate to minimize loss during signal transmission. In the input matching network, \(Z_{S}\) and input impedance of the first stage should match the conjugate. \(Z_{S,M}\) should match \(Z_{S,\mathit{lst},\mathit{Conj}}\) as much as possible, through which, \(Z_{in,\mathit{Matched}}\) is converted to 63.95 \(\Omega\) close to 50 \(\Omega\). The  
Figure 7: Simulation result of (**a**) s-parameters and (**b**) gain. PAE with or without power cell layout parasitic components in the power stage of three-stage PA.  
input matching network was constructed considering the bandwidth rather than exact matching values. Figure 9b,c shows that the output impedances of the first and second stages are conjugate matched with the input impedance of the next stages through the inter-stage matching network. Finally, Figure 9d shows the process of matching \(Z_{L}\) by obtaining the optimized output impedance \(Z_{Opt}\) for maximum output power and PAE. Through load-pull simulation, \(Z_{Opt,Pout}\) and \(Z_{Opt,PAE}\) were found to be \(13+j8.9\)\(\Omega\) and \(12.6+j15.4\)\(\Omega\), respectively. \(Z_{L}\) is converted to \(11+j13.4\)\(\Omega\) through an output matching network (OMN) consisting of transformers and an output parallel capacitor (OPC). When the output impedance corresponds to \(Z_{Opt,Pout}\) and \(Z_{Opt,PAE}\), the maximum output power and PAE are \(15.4\) dBm and \(27.2\%\), respectively, and the expected values with \(50\)\(\Omega\) load impedance are \(15.3\) dBm and \(26.4\%\), respectively. The EM simulation was performed throughout the PA, including matching networks, transformers, pads, and connection lines between devices.  
Figure 8: Schematic of the proposed three-stage PA.  
Figure 9: _Cont_.
# Cooperative Downlink Listening for Low-Power Long-Range Wide-Area Network
## 3 CDL: Cooperative Downlink Listening  
LoRa defines different classes for low-power long-range characteristics, and enables to use the appropriate class according to the requirements of applications. Since most IoT applications operate with battery, LoRa class A is considered as a MAC layer for most applications. Class A enables end devices to transmit data uplink to the network server via gateway, but it also has some limitations and problems for the data reception downlink from network server. In this section, in order to address limitations and problems with LoRa class A, a cooperative downlink listening algorithm is proposed.  
Figure 4: Class A end device slot timing (Adapted from LoRa Specification Ver.1.02, LoRa Alliance, 2016).  
Figure 3: LoRa protocols stack (Adapted from LoRa Specification Ver.1.02, LoRa Alliance, 2016).
# MFCNet: Mining Features Context Network for RGB-IR Person Re-Identification
## 6 Conclusions  
This paper studies the problem of cross-modality person re-identification, which is an important problem in many specific practical monitoring applications. This paper proposes a dual-path attention network architecture MFCNet to learn discriminative shared features and improve the accuracy. An attention module called SGA is designed, and the SGA module is embedded into the backbone network to focus on the key information in input images and mine the feature context. Experiments based on the public SYSU and RegDB datasets verify that the model designed in this paper performs better than the existing state-of-the-art methods with various settings.  
Comprehensive experimental results have demonstrated our approach outperforms state-of-the-art methods, but the accuracy is far away from other recognition tasks such as facial recognition. This paper uses self-attention to mine the pixel-pixel feature context and obtain the shared feature, but ignore the relationship in part within each modality. In the future, we will explore the not only learning of part-aggregated features by mining the contextual part relations within each modality, but also more efficient attention network architectures, to solve RGB-IR Re-ID. The current methods for domain adaptation person re-identification [53; 54; 55; 56] are mostly for the RGB-RGB datasets, and there is little research on RGB-IR datasets. Hence, we will pay attention to the RGB-IR domain adaptation and try to solve this problem.  
Figure 4: Feature Map Visualization. The picture shows two images of one identity. (**a**) uses IR images to search RGB image. (**b**) is the opposite  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Settings** & \multicolumn{4}{c}{**All-Search**} \\ \hline
**Methods** & **r = 1** & **r = 10** & **r = 20** & **mAP** \\ \hline SE & 46.98 & 82.68 & 90.82 & 46.76 \\ \hline CBAM & 47.35 & 84.54 & 91.87 & 46.97 \\ \hline Non-local & 47.50 & 84.39 & 92.14 & 47.65 \\ \hline SGA & 51.64 & 86.80 & 94.66 & 49.99 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of different attention mechanisms.  
**Author Contributions:** Conceptualization, J.M. Methodology, J.M. Writing--original draft preparation, J.M. Writing--review and editing, Y.L. and Y.H. Supervision, H.X. and M.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Insights into the Issue of Deploying a Private LoRaWAN
###### Abstract  
The last decade has transformed wireless access technologies and crystallized a new direction for the internet of things (IoT). The modern low-power wide-area network (LPWAN) technologies have been introduced to deliver connectivity for billions of devices while keeping the costs and consumption low, and the range of communication high. While the 5G (fifth generation mobile network) LPWAN-like radio technologies, namely NB-IoT (narrowband internet of things) and LTE-M (long-term evolution machine type communication) are emerging, the long-range wide-area network (LoRaWAN) remains extremely popular. One unique feature of this technology, which distinguishes it from the competitors, is the possibility of supporting both public and private network deployments. In this paper we focus on this aspect and deliver original results comparing the performance of the private and public LoRaWAN deployment options; these results should help understand the LoRaWAN technology and give a clear overview of the advantages and disadvantages of the private versus public approaches. Notably, we carry the comparison along the three dimensions: the communication performance, the security, and the cost analysis. The presented results illustratively demonstrate the differences of the two deployment approaches, and thus can support selection of the most efficient deployment option for a target application.  
IoT; LPWA; LoRaWAN; LoRa; indoor; private; public +
Footnote †: journal: _Sensors_
# Modeling and Parameter Calibration of Morchella Seed Based on Discrete Element Method
## 3 Results and Discussion  
### Plackett-Burman Test  
The Plackett-Burman (PB) experiment is based on the relationship between the target response and various factors, determining the significant impact of each factor on the response value by comparing the differences at different levels [35]. In this study, an orthogonal testing method was used to construct the regression equation, and Design-Expert software (v.23.1) was employed for parameter optimization to obtain the optimal parameter combination [36]. To quickly and efficiently identify the parameters that significantly affect the repose angle, the range of contact parameters obtained from physical experiments was used as the basis, with each factor set at two levels for analysis. The minimum value was designated as the low level (\(-1\)), and the maximum value as the high level (\(+1\)). The level coding for each parameter is shown in Table 2.  
There are seven design factors of Plackett-Burman trial. Based on the Plackett-Burman trial scheme, 12 groups of trials were conducted, as shown in Table 3.  
The significance analysis of the Plackett-Burman experiment results provided the effects, mean squares, influence rates, and significance rankings for the seven test parameters,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Serial** & **Symbol** & **Parameters** & \multicolumn{2}{c}{**Parameter Levels**} \\
**Number** & & **Low Level (\(-1\))** & **High Level (\(+1\))** \\ \hline
1 & \(A\) & seed–seed collision restitution coefficient & 0.30 & 0.50 \\
2 & \(B\) & seed–seed static friction coefficient (SFC) & 0.20 & 0.70 \\
3 & \(C\) & seed–seed rolling friction coefficient (RFC) & 0.05 & 0.40 \\
4 & \(D\) & seed–seed Johnson–Kendall–Roberts (JKR)(J/m\({}^{2}\)) & 0.01 & 0.10 \\
5 & \(E\) & seed–steel collision recovery coefficient (CRC) & 0.30 & 0.50 \\
6 & \(F\) & seed–steel static friction coefficient & 0.50 & 0.70 \\
7 & \(G\) & seed–steel rolling friction coefficient & 0.05 & 0.50 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Plackett–Burman test parameters of Morrehla cultivation spawn.  
Figure 5: (**a**) The three-dimensional model used for simulations of the seed particles. (**b**) The simulation process of the angle repose.  
as detailed in Table 4. The analysis revealed that the seed-to-seed rolling friction coefficient (C) had the highest influence rate, followed by the seed-to-seed surface energy (\(D\)) and the seed-to-steel rolling friction coefficient (\(G\)), which also showed a significant impact on the response value. The other parameters did not have a significant effect on the repose angle.