esa-sceva/satcom-chunk-collection huggingface dataset:
# Lightweight RT-DETR with Attentional Up-Downsampling Pyramid Network
## 5 Discussion
### Future Research Directions  
Future work should address these limitations and explore several new directions:  
1. Enhanced Data Diversity: Training and testing the model on more diverse datasets collected from various healthcare centers and home environments could improve adaptability and robustness.
2. Model Optimization: Further refinements of the model structure and algorithmic enhancements, potentially drawing inspiration from emerging models like YOLO V11, may boost real-time performance on resource-constrained devices.
3. Cost-Reduction Strategies: Research into more cost-effective hardware integration and model-compression techniques will be crucial for reducing the overall expense of intelligent companion robot systems.
4. Beneficiary-Centric Applications: Future studies should also focus on long-term pilot deployments in Chinese eldercare facilities and community healthcare centers. The primary beneficiaries of this research include elderly individuals, family caregivers, healthcare providers, and policymakers. Intelligent companion robots equipped with our lightweight model could reduce caregiver burden, enable rapid intervention in emergencies, and ultimately lower healthcare costs by preventing severe injuries.  
In summary, while the Lightweight RT-DETR with AUDPN demonstrates significant advancements in both efficiency and detection accuracy, further research is essential to optimize its performance in real-world scenarios and address economic challenges. Our findings not only contribute to the academic discourse on lightweight detection models but also offer practical implications for enhancing healthcare support in China.
###### Abstract  
Background: Functional movement screening (FMS) allows for the rapid assessment of an individual's physical activity level and the timely detection of sports injury risk. However, traditional functional movement screening often requires on-site assessment by experts, which is time-consuming and prone to subjective bias. Therefore, the study of automated functional movement screening has become increasingly important. Methods: In this study, we propose an automated assessment method for FMS based on an improved Gaussian mixture model (GMM). First, the oversampling of minority samples is conducted, the movement features are manually extracted from the FMS dataset collected with two Azure Kinect depth sensors; then, we train the Gaussian mixture model with different scores (1 point, 2 points, 3 points) of feature data separately; finally, we conducted FMS assessment by using a maximum likelihood estimation. Results: The improved GMM has a higher scoring accuracy (improved GMM: 0.8) compared to other models (traditional GMM = 0.38, AdaBoost.M1 = 0.7, Naive Bayes = 0.75), and the scoring results of improved GMM have a high level of agreement with the expert scoring (kappa = 0.67). Conclusions: The results show that the proposed method based on the improved Gaussian mixture model can effectively perform the FMS assessment task, and it is potentially feasible to use depth cameras for FMS assessment.  
injury prevention; FMS; depth camera; Gaussian mixture model; machine learning +
Footnote †: journal: Applied
# Reliability Prediction and FMEA of Loading and Unloading Truss Robot for CNC Punch
## 5 Conclusions and Prospect  
### Conclusions  
1. Using reliability engineering and fuzzy theory, the reliability of the loading and unloading truss robot for CNC punch is studied, and a new reliability prediction method is proposed. Firstly, the failure rate of existing components is used, and the failure rate of an electrical control system is estimated to be 3.146 \(\times\) 10\({}^{-4}\) (1/h) by the component counting method. Secondly, based on the predicted results of electrical control system, the failure rates of the mechanical system and pneumatic system are estimated to be 3.628 \(\times\) 10\({}^{-4}\) (1/h) and 3.715 \(\times\) 10\({}^{-4}\) (1/h), respectively, by using fuzzy theory. Finally, it is estimated that the failure rate of the loading and unloading truss robot for CNC punch is 1.0489 \(\times\) 10\({}^{-3}\) (1/h), and the weak link is the pneumatic system. Therefore, the reliability prediction method proposed in this paper not only makes effective use of the existing reliability data of electrical control system, but also fully considers the actual characteristics of the lack of data of the mechanical system and pneumatic system. Combined with the project experience of relevant experts on the loading and unloading truss robot for CNC punch, the accuracy and operability of reliability prediction are improved;
2. Due to the influence of human factors in the proposed reliability prediction method, the failure frequency, failure mode and main failure causes of each system of the loading and unloading truss robot for CNC punch are calculated through FMEA analysis on the collected fault record data of the loading and unloading truss robot for CNC punch. Among them, the pneumatic system is the most frequent failure system of the loading and unloading truss robot for CNC punch, accounting for 34.86%. Therefore, it is concluded that the pneumatic system is the weak link of the loading and unloading truss robot for CNC punch, which verifies the effectiveness of the reliability prediction method and the accuracy of the predicted results;
3. The reliability prediction method proposed in this paper is consistent with the results of FMEA analysis based on actual maintenance fault record data, which indicates that FMEA analysis can be used to verify the reliability prediction results and provides a new research idea for the combination of reliability prediction and FMEA method.
## 7 Results and Performance Evaluation
### Jammer Type  
We found that the effect of different type of jammers is different on the WSN and conforms to the expected pattern, wherein their effect, in order of decreasing intensity, is: REH, DECH, REN, DECN, RADECH, RADEN, COH, RACH, CON, and RACH. For example, the JIs in respect of Node-13 in the set-up described in Figure 6 were found to be 90.16, 90.15, 90.12, 90.11, 89.68, 89.14, 88.95, 88.72, 77.31, and 75.67 for REH, DECH, REN, DECN, RADECH, RADECN, COH, RACH, CON, and RACH respectively.
# First In-Orbit Validation of Interferometric GNSS-R Altimetry: Mission Overview and Initial Results
## 2 Mission Overview
### Inter-Satellite Cross-Validation Method
#### 2.5.3 Sensial-6 Satellite Radar Altimeter Products  
Sentinel-6, launched on 21 November 2020, operates at a mean altitude of 1336 km with an inclination of 66\({}^{\circ}\). Its primary payload, the Poseidon-4 SAR altimeter, operates in the Ku- and C-band, with the latter primarily used for ionospheric corrections.  
Poseidon-4 provides three types of Level 2 sea surface height (SSH) products: Near-Real-Time (NRT), Short-Time-Critical (STC), and Non-Time-Critical (NTC). These products are disseminated within 2 h, 36 h, and 60 days after data acquisition, respectively. In this study, the Level 2 NTC 1 Hz SSH product (from 1 July to 31 December 2023) was used to evaluate the accuracy of the NSSC iGNSS-R altimeter SSH products.
# Comparison Study on Coarseness Index and Maximum Diameter of Rock Fragments by Linear Cutting Tests
## 5 Comparison of the Correlation with Cutting Results
### Correlation with Specific Energy  
In general, \(SE\) and \(CI\) have an inverse relationship. [PERSON], et al. [14], [PERSON] and [PERSON] [15], and [PERSON], et al. [19] reported that the statistical relationship between \(SE\) and \(CI\) forms Equation (7) for all rock types and cutting tools:  
\[SE=k\cdot CI^{-n} \tag{7}\]  
where \(k\) denotes a parameter related to rock strength and cutting tools and \(n\) denotes a parameter related to the cutting-tool type.  
In [PERSON], et al. [19] and [PERSON] and [PERSON] [15], the relationship between \(SE\) and \(CI\) also formed Equation (7). In contrast, some studies have reported an inverse linear relationship [16; 17; 28; 30]. [PERSON], et al. [18] found that the relationship between \(SE\) and \(CI\) can be linear or nonlinear, depending on the type of rock. The relationship between \(SE\) and particle size parameters is similar to that between \(SE\) and \(CI\). Regarding \(d^{\prime}\) as a representative example, [PERSON] and [PERSON] [15] and [PERSON], et al. [19] reported a nonlinear inverse relationship with \(SE\), and [PERSON], et al. [16] and [PERSON], et al. [28] reported an inverse linear relationship with \(SE\).  
Figure 15 shows the relationship between \(CI\) and \(SE\) obtained in this study. The \(SE\) decreased as the \(CI\) increased for all rock strengths. Although the correlation between them was relatively weak (R\({}^{2}\) < 0.50), they had an inverse linear relationship. The relationship between \(d_{\rm max}\) and \(SE\) is shown in Figure 16. For all rock strengths, \(d_{\rm max}\) increased as \(SE\) decreased with an exponential relationship, and the correlation was strong (R\({}^{2}\) > 0.89). Details of the \(SE\) with respect to cutting conditions are shown in Table 6.  
In the relationship between \(d_{\rm max}\) and \(SE\), when \(d_{\rm max}\) exceeded a specific size, \(SE\) converged to a constant value. In the rocks with a UCS of 20 MPa, when \(d_{\rm max}\) was greater than approximately 25 mm, the \(SE\) was constant at approximately 20 MJ/m\({}^{3}\) (Figure 16a). Even at rock strengths of 30 MPa and 40 MPa, when \(d_{\rm max}\) was greater than 35 mm and 40 mm, the \(SE\) was constant at approximately 22 and 25 MJ/m\({}^{3}\), respectively (Figure 16b,c).  
In summary, predicting excavation efficiency using the change in \(CI\) can only result in predicting an increase or decrease in excavation efficiency, but \(d_{\rm max}\) can be used to determine whether the \(SE\) is close to the minimum value.  
Figure 14: Relationship between maximum diameter and cutting volume: (**a**) rock of UCS 20 MPa; (**b**) rock of UCS 30 MPa; (**c**) rock of UCS 40 MPa.  
Figure 16: _Cont._  
Figure 15: Relationship between _CI_ and _SE_: (**a**) rock of UCS 20 MPa; (**b**) rock of UCS 30 MPa; (**c**) rock of UCS 40 MPa.
# Enhanced IoT-Based End-To-End Emergency and Disaster Relief System
## 5 Experimentation
### Results and Discussion
#### 5.2.4 Interference Score and Noise  
As given by Table 2, the CROW\({}^{2}\) ad hoc network is configured on WiFi Channel 1. Figure 11a shows a sample of the interference score recorded indoors along 25 s. Interference varies from 0-53 dBm (as the maximum peak recorded). We assumed during our previous work [3] that the overall network achievements were affected by the indoor interference caused by WiFi access points, microwaves, etc. Thus, we have recorded the interference score and noise to verify whether these facts affect the overall behavior of the emergency network or not. The recorded interference is important compared to the Received Signal Strength Indicator (RSSI), so the signal is notably affected by the interference. However, the overall interference score is likely to decrease because the wireless infrastructure devices and access points are mostly out-of-order post-disaster. Figure 11b shows a sample of real-time variation for signal and noise strength as a percentage for Channel 1 during 50 s. The noise floor is given by the red curved waves, and the Signal-to-Noise Ratio (SNR) is depicted in yellow color. The figure shows that the signal strength varies between 3 and 50%. To conclude, interference clearly affects the RSSI and, then, the overall performance of the system. Interference is an important factor that must be considered in indoor emergency operations.  
Figure 10: Gyroscope angle variation over 2200 s of the experiment.  
Figure 9: Gyroscope records over 5 min during the experiment. The X-axis is real time.
# Effect of Space Configurational Attributes on Social Interactions in Urban Parks
## 3 Results  
### Social Interactions Observed in Urban Parks  
During the observation, A total of 4983 people involved in 735 social interaction activities were observed, as shown in Table 1. A larger number of involved people (3824, 77%) indicates that social groups are more active than personal groups in urban parks. The number of intimate groups is larger (446, 61%), but the number of people in those groups is relatively small (1159, 23%). The average number of people in the social groups (17.2) is much higher than that in the personal groups (2.6).  
Meanwhile, the intensity of the social interactions depends on the space type. The number of social activities is approximately equal in two spaces: 327 (44%) on pathways and 408 (56%) in activity zones. However, the number of people involved in social activities shows great differences: 1068 (21%) on pathways and 3915 (68%) in activity zones. The reason is that the activity types in different spaces are different. Social group interactions were more frequently observed in activity zones, which had a larger number of people involved (17.2 per group). Meanwhile, the main social activities on the pathways were in personal groups, which commonly contained only 2-3 people (2.6 per group).  
Some qualitative tendencies can be deduced from the inhomogeneous distribution for social activity in Figure 2. Personal groups were observed on almost all of the pathways with varying numbers of involved people. Pathways around the central lake and near  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Activity Type** & \multicolumn{3}{c}{**Space Type**} \\  & & **Pathway** & **Zone** & **Total** \\ \hline \multirow{3}{*}{Number of People} & Personal Group & 643 (13\%) & 516 (10\%) & 1159 (23\%) \\  & Social Group & 425 (9\%) & 3399 (68\%) & 3824 (77\%) \\  & Total & 1068 (21\%) & 3915 (79\%) & 4983 (100\%) \\ \hline \multirow{3}{*}{Number of Activity} & Personal Group & 267 (36\%) & 179 (24\%) & 446 (61\%) \\  & Social Group & 60 (8\%) & 229 (31\%) & 289 (93\%) \\ \cline{1-1}  & Total & 327 (44\%) & 408 (56\%) & 735 (100\%) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of social interaction activities and people observed in two parks.  
the entrance were found to have relatively larger numbers of people involved in personal interactions. Social groups were more commonly observed in activity zones, especially when the activity zones were connected by multiple pathways. Meanwhile, space scales, including the length of pathways and the area of the activity zones, show significant influences on the number of people involved in social interactions. More social interactions were found on long pathways and large activity zones. However, it can also be seen that social interaction intensities in two adjacent spaces could be very different, even they shared very similar locations and scales.  
Figure 2: Spatial distribution of social interaction activities observed in parks.
# Joint Communication-Motion Planning in Networked Robotic Systems
## 2 Conventional Relay-Assisted Robot System
### Task Execution: Act  
#### 2.4.1 Implementation  
In the considered surveillance scenario, two kinds of data should be exchanged between the sensing robot and the relay robot, namely the monitored data sampled by the sensors on the sensing robot, and the control information, e.g., the goal positions of the sensing robot \(x_{\rm s,k}\), the transmit powers that the sensing robots may adopt \(P_{s,k}\). The motion planning algorithm may be operated on the relay robot. In the beginning of each time step, the sensing robot sends the monitored data \(T\eta\) and its goal positions \(x_{s}=\{x_{s,1},x_{s,2},\ldots,x_{s,K}\}\) during the next \(K\) time steps to the relay robot. After the computation of Equation (6), the relay robot sends the allocated transmit power \(P_{s}=\{P_{s,1},P_{s,2},\ldots,P_{s,K}\}\) to the sensing robot, which is used during the communication between the sensing robot and the relay robot.
# Metallic, 3D-Printed, K-Band-Stepped, Double-Ridged Square Horn Antennas
###### Abstract  
This paper presents K-band-stepped, double-ridged square horn antennas fabricated by metallic 3D printing technology in copper (85% copper and 15% stannum) and aluminum alloy (89.5% aluminum, 10% silicon, and 0.5% magnesium). Compared with the popular dielectric 3D-printed horn antenna, the metallic counterpart features better mechanical robustness in terms of material. Moreover, the metallic horns are printed in one piece in one run, different from the dielectric horns that are printed in split pieces and electroplated, they simplify the process and thus result in reduced cost. The agreement between the simulation and measurement results verified the antenna's performance. Both the 3D-printed copper and aluminum alloy antenna have an impedance bandwidth across the K-band, with a maximum gain of 13.23 dBi @ 25 GHz and 13.5 dBi @ 24 GHz, respectively. The metallic, 3D-printed horn antennas are preferable alternatives to replace traditionally-fabricated antennas.  
3D printing; K-band; horn antenna Article
# Wide-Band Wide-Beam Circularly-Polarized Slot-Coupled Antenna for Wide-Angle Beam Scanning Arrays
## 2 _WWCS Antenna Layout_
The top layer (i.e., \(\ell_{3}\)) hosts the _secondary_ element of the antenna, which is implemented as a metallic ring of inner radius \(R_{r}\) and width \(W_{r}\) [Figures 1 and 2c]. Such a parasitic element is \"activated\" by an air coupling mechanism by placing the layer \(\ell_{3}\) at a proper distance \(D\) above the patch (Figure 1). Overall, the total height of the _WWCS_ antenna turns out to be (Figure 1)  
\[T=D+\sum_{l=1}^{\mathcal{L}=3}H_{l}. \tag{1}\]  
The secondary passive element shares a geometric rotational symmetry with the primary active one to obtain a high polarization purity and an azimuth-invariant radiation pattern, which is a highly desirable feature for _WASAS_[11]. Indeed, by properly exciting a _CP_ current within the parasitic ring [20], a torus-shaped pattern with maximum gain on the azimuth plane [i.e., \(\theta=90\) [deg]--Figure 1a)] is radiated. The metallic ring shape is selected to assure that the arising parasitic radiation mode triggers an increased end-fire gain. As a consequence, the combination of the field radiated by the primary element [having maximum gain at broadside, i.e., \(\theta=0\) [deg]--Figure 1a] and the secondary radiator generates a wide beam with half-power beamwidth close to \(HPBW(\varphi)=180\) [deg] along every elevation plane \(\varphi\in[0,\,360]\) [deg] [Figure 1a].
## 3 Drone Swarms as Networked Control Systems
### Non-Interactive Deployment Strategy  
In a non-interactive deployment strategy, each drone is directly connected to a ground control station, which is used to monitor their status (e.g., location, condition of sensors, networking configurations), take decisions and send out new commands, such as new waypoints.  
A non-interactive deployment strategy leads to a simple example of a network that cannot be further divided. This kind of strategy may be regarded as a single NCS considering that each drone can build a controlling closed loop to optimize its operation. The capability to communicate is also indispensable, since the drone still needs to interact with the ground control to complete its system operation of perceiving, communicating, computing and controlling.
## 3 Functional Roles of Nicotinamide and Pyridoxine
### Metabolic, Redox, and Immune-Modulatory Functions in Muscle Aging
#### 3.3.3 Synergistic Antioxidant and Anti-Inflammatory Effects in Muscle Aging  
Both nicotinamide and pyridoxine exert significant antioxidant and anti-inflammatory actions, which are particularly relevant in the context of skeletal muscle aging. Nicotinamide enhances SIRT1 activity, resulting in the deacetylation of NF-kB and subsequent repression of pro-inflammatory cytokine expression, including IL-6, TNF-\(\alpha\), and IL-1B [22; 24; 25; 32]. NAD\({}^{+}\) availability further augments mitochondrial function and improves resistance to reactive oxygen species (ROS)-induced damage by supporting redox cycling and sirtuin-mediated stress responses. Preclinical models have demonstrated that supplementation with nicotinamide or its derivative, nicotinamide mononucleotide (NMN), restores muscle mass, enhances mitochondrial oxidative capacity, and improves physical endurance in aged rodents. These improvements are attributed to restoredNAD\({}^{+}\) levels, enhanced autophagy, and suppression of inflammatory and oxidative pathways [138, 139, 140, 141, 142, 143, 144, 42, 55].  
Clinical observations reveal that low vitamin B6 levels correlate with higher circulating concentrations of C-reactive protein (CRP) and interleukins, including IL-6, and are predictive of reduced grip strength and physical performance in older adults. These findings support a role for vitamin B6 status as both a biomarker and a therapeutic target in muscle aging [18, 41, 59]. While most studies have investigated the effects of nicotinamide and pyridoxine separately, recent preclinical data suggest that their combination may exert additive or even synergistic effects on muscle stem cell activation and regeneration [22]. In particular, [PERSON] et al. demonstrated that co-administration of nicotinamide and pyridoxine enhanced MuSC proliferation and differentiation through \(\beta\)-catenin and AKT signaling. These findings, although preliminary, highlight a promising avenue for combined micronutrient strategies. However, further research, especially in clinical settings, is needed to confirm the magnitude, mechanisms, and practical applications of such synergy [18, 22, 24, 41, 59, 145].  
Building on the mechanistic overview presented above, the table below (Table 1) summarizes the primary molecular targets and pathways through which nicotinamide and pyridoxine exert their effects in aging skeletal muscle. These include redox regulation, mitochondrial maintenance, cytokine modulation, and satellite cell activation--each linked to critical outcomes in muscle regeneration and sarcopenia prevention.
## 4 PFAC Calibration Results
### Jason-3 Cal/Val Results
#### 4.3.1 Jason-3 Cal/Val Results (No. 18)  
Range as well as sea-surface calibrations are carried out for every cycle of Jason-3 Pass No. 18 using the CDN1 and Gavdos Cal/Val sites, respectively. First, the satellite passes over the CDN1 transponder Cal/Val site on the west Crete mountains and about 9 s later it reaches the Gavdos island in the south. Figure 23 presents the latest results in range and sea-surface calibration for Jason-3 over its descending Pass No. 18.  
The sea-surface bias of Jason-3 is determined to be B(A3) = \(-\)4.34 mm, whereas the transponder range bias is B(JA3) = +5.30 mm. As already described, the sign of the Jason-3 bias derived from the transponder and the sea-surface calibrations is opposite. Both calibrations result in the same absolute value of bias, thus supporting the reliability of the derived results.