esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Gridded Precipitation Products
#### 2.3.6 RFEv2  
Finally, RFEv2 is produced by the NOAA-CPC. It is primarily designed for the Famine Early Warning Systems Network to aid in disaster monitoring across Africa [7]. The product estimates daily precipitation for Africa with a spatial resolution of 0.1\({}^{\circ}\). RFEv2 receives data from four operational sources: (1) daily GTS rain-gauge data, (2) Advanced Microwave Sounding Unit (AMSU)-based rainfall estimates, (3) Special Sensor Microwave Imager/Sounder (SSMIS)-based estimates, and (4) the Geostationary Operational Environmental Satellite (GOES) precipitation index (GPI) calculated from cloud-top infrared (IR) temperatures on a half-hourly basis [57]. It is available at [[https://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/.CPC/.FEWS/.Africa/.DAILY/.RFEv2//](https://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/.CPC/.FEWS/.Africa/.DAILY/.RFEv2//)]([https://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/.CPC/.FEWS/.Africa/.DAILY/.RFEv2//](https://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/.CPC/.FEWS/.Africa/.DAILY/.RFEv2//)) (accessed on 9 June 2021). However, the use of polar-orbiting Passive Microwave (PM) and geostationary IR data differs between ARC and RFE. ARC uses 3-hourly IR data rather than 30-min data and does not include PM estimates, whereas RFE does [45; 57].
# Initial Positioning Assessment of BDS New Satellites and New Signals
## 4 SPP Performance  
PDOP availability analyzed in the previous section shows that a global BDS PNT service is available with a mean percentage of 98.84%. In this section, we will explore the SPP performance.
## 3 Methodology
### BATMAN Algorithm
The metric for BATMAN V relies heavily on throughput rather than packet losses. The throughput can be either queried from the interface or _ethtool_ can be used to determine the throughput at any stage. The transmission quality on the local level (between neighbors) can be found by using the numbers of packets sent and echo received to determine a statistical measure for reference to other nodes. This can be achieved by the mechanism and can be denoted using the expression [17]:  
\[TQ=\frac{EQ}{RQ} \tag{1}\]  
The same can be calculated for each neighbor of the node, and all the nodes with their neighbors can calculate the same.
## 3 Energy Efficient Routing
### Principles
#### 3.1.2 Energy Efficient Selection of MPRs  
An energy efficient MPRs selection allows each node \(N\) in the network to select a subset of its one hop neighbors, according to their residual energy, that permits to reach any two-hop neighbor through a path with enough energy. The energy efficient MPR is named EMPR. We keep MPR to denote a MPR selected by the \(OLSR\) protocol.  
To enable a selection of multipoint relays energy-aware, additional information about energy has to be included in the \(Hello\) message. We adopt the usual assumption that interferences are limited to two hops from the transmitter.  
Let \(N\) be any node selecting its EMPRs. For any \(M\) one-hop neighbor of \(N\), \(N\) computes \(M1E(M)\), the weighted residual energy of \(M\) given by:  
\[M1E(M)=min(\frac{E_{R}(M)}{P_{trans}+P_{rcv}},min_{D\in 1 hop(M)}(\frac{E_{R}(D)} {2*P_{rcv}})) \tag{3}\]  
where \(E_{R}(M)\) denotes the residual energy of node \(M\). \(N\) sorts its one-hop neighbors by decreasing order of \(M1E(M)\). Let \(\mathcal{N}1(N)\) denote this ordered set. \(N\) loops around the following actions:  
* if the first node in \(\mathcal{N}1(N)\) covers at least one two-hop neighbor uncovered by the already selected EMPRs, then \(N\) selects this node as EMPR;
* \(N\) extracts this node from \(\mathcal{N}1(N)\).  
until all two-hop nodes are covered.  
In the Equation 3, the weights of \(E_{R}(M)\) and \(E_{R}(D)\) take into account the role played by the nodes \(M\) (one-hop neighbor of \(N\)) and \(D\) (two-hop neighbor of \(N\)) in a transmission from \(N\) to \(D\), via the node \(M\). They represent the maximum transmission duration that can be sustained. We can notice that the algorithm of EMPR selection tends to share the energy consumption between the different nodes. This selection avoids that nodes deplete their battery more quickly than others. However, with regard to the native MPR selection algorithm, this new algorithm introduces more frequent route changes because the residual energy of nodes decreases continuously. To limit these changes, we introduce two thresholds \(ThresholdEMPR\) and \(ThresholdMinEnergy\) and change the EMPR selection only if these two following conditions are met:  
1. there is at least one 2-hop neighbor \(D\) such that: \(\frac{E_{R}(new\_EMPR)-E_{R}(old\_EMPR)}{E_{R}(old\_EMPR)}>ThresholdEMPR\), where \(E_{R}(new\_EMPR)\) represents the residual energy of the new node selected as EMPR to cover \(D\), and \(E_{R}(old\_EMPR)\) represents the residual energy of the previous node selected as EMPR to cover \(D\).
2. the residual energy of the new EMPR is sufficient: \(E_{R}(new\_EMPR)\geq ThresholdMinEnergy\). This avoids frequent changes when the residual energy of an EMPR tends to 0.  
If these two conditions are met for at least one 2-hop neighbor, the previous set of EMPRs is changed by the new set. Each node computes its EMPRs before sending its periodic \(Hello\).
## 5 Experimental Verification
### Verification of the Robot Mobility as Well as the Hydrodynamic Model  
The locomotion of the robot was examined in the stroke cycle of \(T=1.6\) s when the paddles performed an ellipse-like spatial stroke trajectory continually. The length of the pool was 3 m and its width was 2 m. The robot completed two different motions, going straight and turning. Similarly, the angular velocities of the stepper motors and the linear stepper motors were set to be the same as shown in Figure 4. When the robot went straight and turned, the rotational directions of the stepper  
Figure 16: The experimental record of the stroke mechanism sliding on the water from the viewpoint of one side.  
Figure 17: Experimental and theoretical trajectory curves of point \(f\): (**a**) in X–Z plane; (**b**) in Y–Z plane.  
motors on the stroke mechanisms on both sides were set to be opposite and the same, respectively. Figure 18 is the path of the robot's locomotion progress within 6 s when the robot reached a dynamic balance. It can be seen that the trajectories of five feature points on the supporting legs and the center of the robot are almost straight lines, which shows that the robot has a good marching ability and can perform in a straightforward motion. By calculating the distance traveled by the robot in 6 s, the average speed of the robot moving forward was 0.129 m/s. By comparing the body-length of the robot, the robot can reach 0.39 BL/s. Figure 19 shows photo snapshots of the robot turning right within 8 s. It can be seen that the robot rotates almost around its own center. The robot turned 180\({}^{\circ}\) in 8 s, thus the turn speed is 22.5\({}^{\circ}\)/s. The two figures show that whether the robot goes straight or turns, the robot has good mobility.  
Figure 19: The positions of the robot at different times t: (**a**) 0 s, (**b**) 1 s, (**c**) 2 s, (**d**) 3 s, (**e**) 4 s, (**f**) 5 s, (**g**) 6 s, (**h**) 7 s, and (**i**) 8 s, when the robot is turning right.  
Figure 18: The path of the robot’s progress locomotion within 6 s. The five lines express five feature points on the legs and the center of the robot.  
In addition to testing the robot motion with a stroke cycle of 1.6 s, we also conducted forward motion experiments on the robot with the stroke cycles of 2.0, 2.4, 2.8, and 3.2 s. According to different stroke cycles, the angular velocities of the stepper motors and the linear stepper motors were set with reference to the velocity ratio in Figure 4. Figure 20 shows photo snapshots of the robot moving forward within 6 s when the stroke cycle is \(T\) = 2.0 s. The displacement of the robot was measured by the ruler next to it. By recording the motions of the robot in other stroke cycles, the average speeds of the robot in different stroke cycles \(T\) can be calculated--shown in Figure 21. It can be found that the speeds of the robot measured by the experiments are relatively close to the theoretical value which proves the applicability of the hydrodynamic model.
## 5 Conclusions
Furthermore, to evaluate the robustness and adaptability of the proposed framework under realistic conditions, we conducted additional experiments using datasets constructed from long-term satellite observations spanning 2021 to 2024. These datasets naturally incorporate variations in signal propagation environments, satellite hardware conditions, and ground station configurations. Without retraining the model from scratch, we fine-tuned the pretrained encoder using only a small number of labeled samples from each new dataset. The results show that while distributional shifts can lead to reduced accuracy under extremely low-shot conditions, the model quickly recovers high identification performance as more labeled samples become available. These findings demonstrate the strong generalization capability of the proposed framework and confirm its practical feasibility in dynamic and evolving environments. While the proposed framework demonstrates strong performance, it still relies on fine-tuning with a small amount of labeled data. As a result, it may not generalize immediately to entirely new emitter classes without some form of supervised adaptation. This limitation is particularly critical in scenarios involving previously unseen emitters, where the current method lacks the capability for instant recognition. In future work, considering the emergence of such unknown targets, we plan to incorporate domain adaptation and incremental learning techniques to enable the model to continuously adapt to new data while preserving previously acquired knowledge. Conceptualization, J.W. ; methodology, J.W. and L.G. ; software, J.W. and H.Z. ; validation, J.W., H.Z. and L.G. ; formal analysis, [PERSON], P.L. and P.S. ; investigation, [PERSON]; resources, [PERSON], L.G. and X.L. ; data curation, H.Z. and L.G. ; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON], H.Z., L.G., P.L. and P.S. ; visualization, [PERSON]; supervision, [PERSON], L.G. and X.L. ; project administration, H.Z. and L.G. ; funding acquisition, H.Z. All authors have read and agreed to the published version of the manuscript. This work was supported by the Technical Support Talent Plan of the Chinese Academy of Science (Grant No. E317 YR17). The datasets presented in this article are not readily available because the data are part of an ongoing study. Requests to access the datasets should be directed to the author at [EMAIL_ADDRESS]. The authors declare no conflict of interest.
## 3 Results
### Visual Reliance  
Figure 4 and Table 4 presents the %modulation results. We found significant decreases in %modulation after the fatigue protocol for TTB AP means (PRE: 48.7 \(\pm\) 16.54; POST: 21.37 \(\pm\) 11.65, \(p\) < 0.001), TTB ML SD (PRE: 49.17 \(\pm\) 18.23; POST: 34.73 \(\pm\) 24.66, \(p\) = 0.05), and TTB AP SD (PRE: 44.95 \(\pm\) 20.02; POST: 22.67 \(\pm\) 12.83, \(p\) < 0.001). However, TTB ML means did not differ significantly between pre- and post-fatigue (PRE: 44.59 \(\pm\) 15.64; POST: 40.52 \(\pm\) 13.57, \(p\) = 0.119).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**\%Modulation** & **Pre-Fatigue** & **Post-Fatigue** & **\(t\)** & **95\% CI** & \(d\) & \(p\) \\ \hline TTB ML & 44.59 \(\pm\) 15.64 & 40.52 \(\pm\) 13.57 & 1.61 & (\(-\)0.078 to 0.681) & 0.28 & 0.119 \\ \hline TTB AP & 48.7 \(\pm\) 16.54 & 21.37 \(\pm\) 11.65 & 7.604 & (0.899 to 1.962) & 1.91 & \textless{}0.001 \\ Mean of minima & 49.17 \(\pm\) 18.23 & 34.73 \(\pm\) 24.66 & 3.033 & (0.168 to 0.969) & 0.66 & 0.05 \\ \hline TTB AP & 44.95 \(\pm\) 20.02 & 22.67 \(\pm\) 12.83 & 4.441 & (0.401 to 1.266) & 1.33 & \textless{}0.001 \\ \hline \hline \end{tabular}  
* Abbreviations: AP, anteroposterior; ML, mediolateral; SD, standard deviation; TTB time to boundary.  
\end{table}
Table 4: Mean (\(\pm\)SD) for %modulation changes between pre- and post-fatigue for the time-to-boundary (TTB) measures.  
Figure 3: Effects of fatigue and vision on postural control by two-way ANOVA. Abbreviations: AP, anteroposterior; ML, mediolateral; SD, standard deviation; TTB, time to boundary. * Indicates a statistically significant time by visual condition interaction (_p_ < 0.01). ** Indicates a statistically significant time by visual condition interaction (_p_ < 0.001).
## 5 SINR-Based TPC as a Potential Game
### System Model  
A THz multi-hop communication network is considered to comprise multiple nodes, denoted by \(N\). The network operates in an environment where high-density node deployments and obstacles introduce challenges, including interference and signal degradation. Hence, intermediate nodes are strategically deployed to mitigate communication obstacles and extend the range of transmissions.  
Amplify-and-Forward (AF) and Decode-and-Forward (DF) relaying can be applied in THz multi-hop systems, with the choice depending on the network topology, application requirements, and available resources. In this paper, a game-theoretic framework for relaying is proposed to dynamically select transmission strategies based on network conditions and resource constraints. Note that the directional antennas may be utilized for beamforming in THz communications, which also assist mitigation applications.  
The SINR for the transmission between nodes \(x_{1}\) and \(x_{2}\) remains:  
\[SINR_{x_{1},x_{2}}=\frac{P_{R_{x}}}{I_{N}+S_{N}}, \tag{4}\]  
where \(P_{R_{x}}\) is the received signal power from \(x_{1}\) to \(x_{2}\), \(I_{N}\) is the aggregate interference power from nearby transmitting nodes, and \(S_{N}\) is the aggregate noise power.  
Incorporating the effect of path loss, fading, and directional gains:  
\[P_{R_{x}}=P_{T}G_{T}G_{R}L_{f}^{-1}, \tag{5}\]  
where \(P_{T}\) is the transmitted power from \(x_{1}\), \(G_{T},G_{R}\) is the transmit and receive antenna gains, and \(L_{f}\) is the path loss, which may depend on distance \(d\) and frequency \(f\) (e.g., \(L_{f}=c_{1}d^{a}f^{b}\)). The interference power (\(I_{N}\)) is given by,  
\[I_{N}=\sum_{j\in\mathcal{I}}P_{T_{j}}G_{T_{j}}G_{R_{j}}L_{f_{j}}^{-1}, \tag{6}\]  
where \(\mathcal{I}\) represents the set of interfering nodes.  
The thermal noise power plus other environmental factors is given by,  
\[S_{N}=k_{B}TB, \tag{7}\]  
where \(k_{B}\) is the Boltzmann constant, \(T\) is the system temperature, and \(B\) is the bandwidth.  
Here, \(SINR_{x_{1},x_{2}}\) represents the SINR of the transmission from node \(x_{1}\) to node \(x_{2}\). The calculation of \(SINR_{x_{1},x_{2}}\) is given by (4). In this equation, \(P_{R_{x}}\) represents the received signal power, \(I_{N}\) is the aggregate power of the interferers, and \(S_{N}\) is the aggregate noise.
# FairRAG: A Privacy-Preserving Framework for Fair Financial Decision-Making
## 5 Experimental Setup
### Datasets  
The datasets used in our experiments contain customer-related features and churn labels. Below is a brief description of each dataset:  
* Bank Churn Dataset: Contains customer demographics, account information, and transaction history to predict whether a customer will churn.
* Telco Churn Dataset: Comprises customer service data, contract details, and billing information to assess the likelihood of a customer discontinuing the service.  
All datasets contain both numerical and categorical features. Categorical features were encoded using one-hot encoding, while numerical features were standardized to ensure a uniform scale.
## 2 Instrument Design
### Monitoring and Control Systems
#### 2.3.3. Thermal Monitoring  
In order to measure periodically the temperatures of the matched loads and the amplifiers of each radiometric band and polarization, 32 DS18B20 digital temperature sensors (one for each of the 16 amplifiers and 16 matched loads, Maxim Integrated, CA, USA) are used.These sensors provide temperature measurements with an accuracy of \(\pm\)0.5 \({}^{\circ}\)C. The sensor readings are controlled by a microcontroller which make the data available to the host computer via an Ethernet connection. Then, once the temperature values are retrieved, they can be used to calibrate and correct the radiometric measurements.  
Figure 6: Side view of MERITXELL’s enclosure (**Left**); and front view the antenna radome (**Right**).
# FlickPose: A Hand Tracking-Based Text Input System for Mobile Users Wearing Smart Glasses
## 2 Related Work  
### Text Input Methods for XR Users  
In recent years, however, there are also methods that use HMDs and other sensors to acquire data on the HMD user's body and use body movements to operate XR applications when wearing an HMD. Typical body data used for manipulation when wearing an HMD include hand data and gaze data.  
Since methods using controllers are generally superior to those using the hand or eye gaze in terms of accuracy, research has been conducted on new character input methods using these devices. For example, [PERSON] et al. [4] proposed a method for character input by arranging keys in a circular pattern on a controller touchpad. [PERSON] et al. [5] and [PERSON] et al. [6] proposed method to operate a touch-panel on the front of the HMD. As examples of using other devices, [PERSON] et al. [7], [PERSON] et al. [8], and [PERSON] et al. [9] proposed methods using a smartphone for text input. On the other hand, [PERSON] et al. [10] pointed out that while the method of pointing a controller at a virtual keyboard is superior in performance, it may lead to fatigue after long hours of typing. In addition, these methods are not suitable for mobile users, as the controllers need to be carried separately from the HMD when used.  
As a method using gaze data, [PERSON] et al. [11] proposed combining gaze and hand movements for flick input. [PERSON] et al. [12] compared methods for inputting text using eye gaze only, which involved inputting text using dwell, blinks, and eye movement. However, methods using eye gaze data have not gained widespread acceptance due to the high cost of devices equipped with eye trackers.  
Therefore, in implementing a text input system, we use hand data from the viewpoint that data can be acquired by the HMD alone and are expected to be widely used in the future from a price standpoint. Since current smart glasses are not equipped with a hand tracker of sufficient accuracy, Meta Quest 3 will be used as the device to implement the character input system.  
Meta Quest 3 analyzes images from the headset's built-in camera to estimate the position and orientation of a total of 24 joint points on the fingers, arms, legs, and torso; by using the Oculus Integration SDK, this information can be obtained in Unity.
## 3 Proposed Methodology  
### System Overview  
The proposed embedded system-based HAR model uses a spatial feature map extracted using CNN through the weighted mean and the temporal feature extracted via frame change rate, and then, generates spatiotemporal features. The generated spatiotemporal features are entered into a multilayer perceptron (MLP), which is lighter than the existing networks that were used in the HAR models, outputting action classes and schematizing the block diagram of the entire system, as shown in Figure 2. Image sequences, which make up a video, entered into a model, are converted into N frame stacks (\(FS^{N}\)) with random intervals and extracted via CNN into feature map (\(FM_{K}^{N}\)) with spatial features (\(K\) means the length of a feature map that is flattened in one dimension). Here, CNN is used as a feature extractor to extract a spatial feature map, and the fully connected layer connected to the end of the CNN is removed. \(FS^{N}\) extracts frame difference (\(FD^{N}\)) of each frame constituting \(FS^{N}\) as a temporal feature, since the sequential frames within a video contain time information with sequentially stacked data. Finally, to fuse spatial and temporal features, \(FM_{K}^{N}\) is weighted and averaged to \(FD^{N}\) according to the interval of \(FS^{N}\) converted from video to generate feature vectors (\(FV_{K}\)) with spatiotemporal features and input them into MLP. The MLP, which is independent from CNN, outputs predefined action classes and schematized the process of recognizing action by receiving video data, which is shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Modality** & **Advantage** & **Disadvantage** \\ \hline \multirow{2}{*}{Depth} & Clearly separate foreground and & \\  & background & \\ \hline \multirow{2}{*}{Skeleton} & Extract correlations from joint points & \\  & connected by body structure & \\ \hline \multirow{2}{*}{Vision} & Includes various visual information & \\  & with high resolution & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of advantages and disadvantages of data modality for behavior recognition.  
Figure 2: Block diagram of the proposed weighted mean-based human action recognition (HAR) model.
## 4 Discussion
### Modifiable Risk Factors
#### 4.1.3 Muscular Strength  
There was no significant correlation identified between the variables assessed via isokinetic dynamometry and ACLI in the elite soccer population [39]. In contrast, [PERSON] et al. [56] assessed similar isokinetic strength data in female youth basketball and soccer populations, reporting a Q-H ratio of 1.78 (95%CI: 1.06-2.70) predicted high knee abduction moment in laboratory testing, inferring increased ACLI risk [56]. However, [PERSON] et al. [57] in their prospective study concluded that isokinetic quadricep and hamstring strength and the H-Q ratio were not associated with increased ACLI risk in elite female handball and soccer athletes and should not be used for ACLI risk screening in this cohort [57]. Stating that, the isokinetic dynamometry of the quadriceps and hamstrings as a screening test for ACLI offers little predictive value for clubs in elite field-based team sport [57].  
Alternative strength testing methods, using handheld dynamometry to measure isometric hip strength has been reported to be useful in other prospective studies [57,58]. [PERSON] et al. [58] established that increased ACLI risk is significantly associated with reduced hip external rotation strength (OR: 1.23, 95%CI: 1.08-1.39, \(p=0.001\)) and hip abduction strength (OR: 1.12, 95%CI: 1.05-1.20, \(p=0.001\)) in male and female non-professional athletes from various sports [58]. In contrast, [PERSON] et al. [57] reported no association with isometric hip abduction strength and ACLI risk in elite female handball and soccer athletes.  
The conflicting results amongst studies, although notably primarily in female athlete cohorts, do provide some direction for further research on screening hip strength versus knee strength variables with ACLI risk in elite male field-based team sport athletes. Comparing isokinetic dynamometry or a force frame with handheld dynamometry to assess hip strength variables could be useful to determine if the tool is a differentiating confounding factor impacting results rather than the muscles assessed. The cost-effectiveness, time-efficiency, and reproducibility of handheld dynamometry could deem this a useful screening tool and testing methodology for clubs, although internal validity needs close consideration if there are multiple assessors performing the test. However, it must be highlighted to the reader that the authors are in no way diminishing the role that muscular strength has as a significant tool for injury prevention [59]; it is simply that isolated joint assessments of torque production are not generally significant factors for determining ACLI risk. Developing strength as a multi-joint compound movement parameter remains important to incorporate through resistance training programs in the training of all athletes.