esa-sceva/satcom-chunk-collection huggingface dataset:
# Validity of the Enode Sensor and My Jump 3 App for Assessing Countermovement Jump Performance
###### Abstract
Likewise, mobile applications, such as My Jump, utilize smartphone cameras and advanced video analysis algorithms to estimate jump height and other CMJ metrics, thus making performance assessment more accessible and practical for day-to-day use by athletes and coaches [16]. Although these tools offer convenience, they may also face challenges in accuracy, especially in uncontrolled environments, where factors such as surface variations and user positioning can impact data reliability [15; 17]. A considerable body of research has explored the accuracy and reliability of the My Jump application and various wearable devices in jumping assessments [17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31]. Validity, which measures how well a tool assesses its intended construct, and reliability, which concerns the consistency of repeated measurements, are central to evaluating these tools [32; 33]. Several studies have mentioned that My Jump can provide valid, reliable jump height measurements comparable to those from Force Plates in controlled environments, making it a practical alternative for settings beyond the lab [17; 34; 35; 36]. Wearable devices, such as accelerometers and inertial measurement units, have also demonstrated promising accuracy, though factors like sensor placement and calibration can affect data quality [4; 37]. Research on the Enode Plus (formerly VmaxPro) for measuring vertical jump height remains comparatively limited, with no studies to date directly assessing its validity against both the My Jump 3 Application and the Force Plate standard. The aim of this study is to evaluate and compare the accuracy and reliability of CMJ data obtained from the Enode Sensor and My Jump 3 Application against those obtained from a gold-standard Force Plate. Specifically, this study sought to answer (1) how CMJ data from the Enode Sensor compare to Force Plate data in terms of accuracy and reliability, (2) how CMJ data from the My Jump 3 App compare to Force Plate data, and (3) how the validity and reliability of the Enode Sensor compare directly to those of My Jump 3.
# Dynamic Synthesis of Three -- Point Circle Peripheral Docking Technology Pose
## 3 Visual Positioning System
### Modules and Algorithms
It is assumed that the pixel coordinates of the point p under the image coordinate system are (\(u\), \(v\)), and the coordinates of the point under the camera coordinate system are (\(x_{d}\), \(y_{d}\), \(z_{d}\)), satisfying the following equation:  
\[z_{d} \begin{bmatrix}u\\ v\\ 1\end{bmatrix} = \begin{bmatrix}\alpha_{x}&0&u_{o}&0\\ 0&\alpha_{y}&v_{o}&0\\ 0&0&1&0\end{bmatrix}\begin{bmatrix}x_{d}\\ y_{d}\\ z_{d}\\ 1\end{bmatrix} =M_{1} \begin{bmatrix}x_{d}\\ y_{d}\\ z_{d}\\ 1\end{bmatrix}  \tag{1}\]  
The matrix \(M_{1}\) represents the camera internal parameter matrix. Rigid transformation between the camera coordinate system and the landmark coordinate system is completed using the rotation matrix \(R\) and the translation vector \(t\). The equation is as follows:  
\[ \begin{bmatrix}x_{d}\\ y_{d}\\ z_{d}\\ 1\end{bmatrix} = \begin{bmatrix}R&t\\ 0^{T}&1\end{bmatrix}\begin{bmatrix}x_{p}\\ y_{p}\\ z_{p}\\ 1\end{bmatrix} =M_{2} \begin{bmatrix}x_{p}\\ y_{p}\\ z_{p}\\ 1\end{bmatrix}  \tag{2}\]  
The rotation matrix \(R\) and the translation vector \(t\) are unknown quantities to be solved, and \(M_{2}\) represents the transformation parameter matrix of the camera coordinate system and landmark coordinate system. Substituting Equation (2) into Equation (1) yields  
\[z_{c} \begin{bmatrix}u\\ v\\ 1\end{bmatrix} = \begin{bmatrix}\alpha_{x}&0&u_{o}&0\\ 0&\alpha_{y}&v_{o}&0\\ 0&0&1&0\end{bmatrix}\begin{bmatrix}R&t\\ 0^{T}&1\end{bmatrix}\begin{bmatrix}x_{p}\\ y_{p}\\ z_{p}\\ 1\end{bmatrix} =M_{1}M_{2} \begin{bmatrix}x_{p}\\ y_{p}\\ z_{p}\\ 1\end{bmatrix}  \tag{3}\]  
It is assumed that the coordinates of the location identification marking points O, A, and B under the landmark coordinate system are (\(x_{p}\), \(y_{pi}\), \(z_{pi}\)) (\(i\) = 1, 2, 3), and the image coordinates are (\(u_{i}\), \(v_{i}\)) (\(i\) = 1, 2, 3). Substituting these coordinates into Equation (3) yields three equations:  
\[z_{ci} \begin{bmatrix}u_{i}\\ v_{i}\\ 1\end{bmatrix} =M_{1}M_{2} \begin{bmatrix}x_{pi}\\ y_{pi}\\ z_{pi}\\ 1\end{bmatrix} \quad i=1,2,3 \tag{4}\]  
Then, the optimization algorithm is used to solve the rotation matrix \(R\) and the translation vector \(t\), and the coordinate of the target vehicle's landmark coordinate system's origin under the camera coordinate system of the active vehicle is calculated as \(t\), while the coordinate of the camera coordinate system's origin of the active vehicle under the target vehicle landmark coordinate system is \(-R^{T}t\).
# A Fault Analysis Method Based on Association Rule Mining for Distribution Terminal Unit
## 5 Experiment and Results
### Strong Rules of DTU Fault Data
#### 5.2.2 Fault Repairing  
When DTU offline happens, a fault usually exists in the CPU module, communication module, or control circuit module. Based on the rank of rule confidence, the CPU module is investigated at first; then, the communication module and control circuit module are checked. When a remote signaling fault happens, a fault usually exists in the control circuit or communication module. These five rules form the initial diagnosis rule base. During fault repairing, modules in the diagnosis rule base should be inspected first. By these means, fault elimination time can be shortened.  
Strong rules involving brand information were not included in the above two rule bases, but they could be used to inform manufacturers about family defects of their products. These rules and their confidence values are shown in Figure 7.  
For instance, brand 2 should pay attention to the CPU and communication modules of its products as well as the DTU offline issue. Brand 4 and brand 10 should improve control circuit modules of their products and deal with remote signaling faults.
# Re-Keying Scheme Revisited: Security Model and Instantiations
## 3 SCA on the Previous Re-Keying Schemes
### Previous Works  
[PERSON] et al. [4] introduced the design concept of \"separation of duties\" and proposed a concrete re-keying scheme suitable for lightweight devices. Their scheme consists of two parts as in Figure 5. The first part is a re-keying function \(F\), which takes a master secret key \(mk\) and randomness \(r\) as inputs and outputs a session key \(tk\). The re-keying function is designed with simple operations, which is easily protected from the SCAs. The second part is a block cipher \(BC\), which encrypts a plaintext \(m\) with the session key \(tk\). Decryption consists of the above \(F\) and \(BC^{-1}\), which is the inverse of \(BC\). They assumed that \(F\) was multiplicative; precisely, \(tk=mk\cdot r\) in a finite field. Against their scheme, [PERSON] et al. [5] showed an attack that first searches \(tk\) with the birthday attack and then recovers \(mk\) by \(mk=tk\cdot r^{-1}\).  
[PERSON] et al. [6] then gave two other schemes and discussed their security. Figure 6 depicts their first scheme. Their first scheme is an improvement of [PERSON] et al.'s scheme by replacing the re-keying function with a non-invertible function. Figure 6 depicts their construction, consisting of an SCA-resistant function \(g\) and a one-way function \(h\).  
Figure 4: Experiments for the tweakable block cipher.
## 5 Results
### Analysis
#### 5.2.3 Channel Access Delay  
Figure 8a describes the channel access delay between the MAC protocols for a three-channel configuration. It was measured that the channel access delay for IEEE 802.11 was approximately 206% higher than the E-SA-MMAC protocol at 70 stations. This is due to IEEE 802.11 having only a single channel for data transmission, incurring a greater channel access delay for stations that have packets to send. To better represent the differences among the MMAC protocols, we removed IEEE 802.11 from the analysis.  
In Figure 8b, the E-SA-MMAC protocol has the lowest channel access delay, followed by the second protocol, Max-E-SA-MMAC, then SA-MMAC, and the other protocols. This can be attributed to the pure bidirectional mode of operation, even in the control channel, with the protocol. More stations have a chance to piggyback a data packet with the acknowledgment.  
In Figure 8c, AMMAC has the highest delay in channel access, whilst the other protocols are fairly equal in performance. The reason for this, as explained previously, is due to the single data packet transmission per handshake in the AMMAC protocol, while the other protocols allow for bidirectional data transmission per handshake. Figure 8d shows BiMMAC with the lowest channel access delay as the number of stations increases due to less congestion at the control channel.
## 7 Conclusions  
Taking the cost and the state estimation accuracy into consideration, a multi objective method of optimal PMU placement is proposed in this paper. The following conclusions are obtained. The network compression based on the pre-configuration and existing measurements can not only reduce the number of PMUs of the most economical solution and the compromised solution, but also reduce the state estimation error under the same number of PMUs. Furthermore, the proposed OPP method can provide the most economical solution with the minimum number of PMUs required for the full observability and the compromised solution which considers the cost and state estimation accuracy comprehensively. Moreover, the compromised solution obtained by the proposed OPP method can effectively reduce the voltage amplitude estimation error and voltage phase angle estimation error. In addition, the most economical solution obtained by the proposed OPP method has the minimum number of PMUs required for the full observability than those provided by other methods. The expected future work will take the influence of network reconfiguration into consideration.  
[PERSON] and [PERSON] conceived the idea of the study and conducted the research. [PERSON] analyzed most of the data and wrote the initial draft of the paper. [PERSON] and [PERSON] contributed to finalizing this paper.  
This research was funded by the National Key Research and Development Program of China, grant number 2017 YFB0902900 and 2017 YFB0902902.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 2 VDES and Maritime Wireless Channel
### Maritime Wireless Channel  
As a traditional logarithmic path loss model for wireless communication such as LTE, Wi-Fi cannot represent the local oscillations owing to the rays reflected from the sea surface and extra refraction owing to the evaporation duct in the maritime wireless channel. As shown in Figure 2, RF propagation in the maritime environment is affected by various factors, such as antenna height changes due to waves and effects from evaporation ducts. Therefore, several studies have been conducted on wireless channel models for over-the-sea  
Figure 1: VDES functions and frequency use case.  
communication [21; 22; 23; 24; 25]. [PERSON] et al. focused on the evaporation duct phenomenon, which occurs owing to variations in humidity across the air-sea boundary. Moreover, channel modeling was performed in such an environment; special attention was paid to refraction caused by a possible evaporation duct as well as direct rays and sea surface-reflected rays [21]. [PERSON] et al. established the statistical distribution of path loss based on varying distances from those in the literature, proposed a 2-P probability distribution model, and proved that it fits well in marine communication channel modeling [25]. [PERSON] et al. proposed a practically usable deterministic path loss model, demonstrating its ability to sufficiently simulate the maritime wireless channel environment [26; 27]. In this study, we utilize the two-ray path loss and three-ray path loss models proposed and validated in [26; 27], which are expressed in Equations (1)-(3).  
\[PL_{2 ray}(h_{t},h_{r},d)=-10\log\left(\left(\frac{\lambda}{4\pi d}\right)^{2} \cdot 2 sin\left(\frac{2\pi h_{t}h_{r}}{\lambda d}\right)\right) \tag{1}\]  
In Equation (1), \(h_{t}\) and \(h_{r}\) denote the heights of the transmitter and receiver antennas, respectively. Moreover, \(\lambda\) indicates the wavelength of the carrier signal, and \(d\) indicates the distance between the transmitter and receiver.  
\[PL_{3 ray}(h_{t},h_{r},h_{e},d)=-10\log\left(\left(\frac{\lambda}{4\pi d} \right)^{2}\cdot 2(1+\varnothing)^{2}\right) \tag{2}\]  
In Equation (2), \(h_{e}\) denotes the height of the effective evaporation duct, and \(\varnothing\) is calculated as follows:  
\[\varnothing=2 sin\left(\frac{2\pi h_{t}h_{r}}{\lambda d}\right)\cdot sin\left( \frac{2\pi(h_{e}-h_{t})(h_{e}-h_{r})}{\lambda d}\right) \tag{3}\]  
Figure 3 illustrates the signal path loss as a function of distance, utilizing both the two-ray and three-ray models, and allows us to observe the phenomenon of a deep null (sudden signal drop).  
Figure 2: RF propagation in the maritime environment.
## 2 Construction of the Spatiotemporal Network for Subway Vehicle Maintenance and Scheduling
### Fundamental Elements
Illustrations of the maintenance, scheduling, and state transition arcs are provided in Figures 1-3. 3. Spatiotemporal network paths: Within the subway vehicle maintenance and scheduling spatiotemporal network, the operational paths of subway vehicles are formed by sequentially connecting operation start nodes to operation end nodes, incorporating all necessary operational steps. Given that multiple operational sequences may be available following the completion of each task, each subway vehicle can follow multiple possible paths within the network. By linking the operational sequences chronologically, the operational paths of subway vehicles can be visualized (Figure 4). This representation enables a clearer depiction of subway vehicle operations within the spatiotemporal network, elucidating the interconnections and sequential relationships among different operational phases. Such insights contribute to optimizing subway vehicle scheduling plans and improving operational efficiency. Path 1 represents a scenario where the train starts from the initial node, undergoes scheduling operations, and continues executing intermediate connection tasks cyclically until the planned period ends, ensuring continuous operation. Path 2 describes a case where the train initially performs scheduling tasks, transitions into maintenance operations upon reaching the maintenance cycle, and resumes scheduling until the end of the planned period. Path 3 illustrates a situation where the train begins with scheduling operations, undergoes  
Figure 4: Space–time network path diagram. Figure 3: State transition arc diagram. Figure 2: Scheduling operation arc diagram. maintenance at a later stage, and subsequently enters a storage track in standby mode until the plan concludes. Path 4 depicts a scenario where the train remains inactive throughout the planned period, with a direct connection from the initial to the final node, indicating that no operations are executed. The selection of different paths in the spatiotemporal network reflects various scheduling strategies under different conditions, facilitating more effective planning and coordination of subway train operations.
###### Abstract  
Microseismic (MS) signals recorded by sensors are often mixed with various noise, which produce some interference to the further analysis of the collected data. One problem of many existing noise suppression methods is to deal with noisy signals in a unified strategy, which results in low-frequency noise in the non-microseismic section remaining. Based on this, we have developed a novel MS denoising method combining variational mode decomposition (VMD) and Akaike information criterion (AIC). The method first applied VMD to decompose a signal into several limited-bandwidth intrinsic mode functions and adaptively determined the effective components by the difference of correlation coefficient. After reconstructing, the improved AIC method was used to determine the location of the valuable waveform, and the residual fluctuations in other positions were further removed. A synthetic wavelet signal and some synthetic MS signals with different signal-to-noise ratios (\(SNR\)s) were used to test its denoising effect with ensemble empirical mode decomposition (EEMD), complete ensemble empirical mode decomposition (CEEMD), and the VMD method. The experimental results depicted that the \(SNR\)s of the proposed method were obviously larger than that of other methods, and the waveform and spectrum became cleaner based on VMD. The processing results of the MS signal of Shuangjiangkou Hydropower Station also illustrated its good denoising ability and robust performance to signals with different characteristics.  
microseismic signal; noise suppression; variational mode decomposition; akaike information criterion +
Footnote †: journal: Applied Sciences
## 2 Results and Discussion
### Application in Biological Samples
#### 2.4.2 Concentration of Piperacillin and Imipenem in Plasma
The observed concentrations of impienem in plasma 1 h after infusion were below the SmPC values, ranging from 5.8 to 38.8 mg/L (Table 8). A positive correlation between concentrations of both antibiotics in plasma before and after infusion was found (Figure 5), whereby the Pearson Correlation Coefficient reached the value of r = 0.9000 for piperacillin and r = 0.8389 for impienem. A negative tendency between the concentrations of unbound \(\upbeta\)-lactams in plasma after infusion and albumin level in blood was observed for piperacillin and impenem; nevertheless, the Pearson Correlation Coefficients exhibited relatively low values (r = \(-\)0.6617 and r = \(-\)0.4132, respectively), indicating a weak relationship between these variables. A similar observation was reported by [PERSON] et al. [13], who also could not find a significant dependence between albumin concentrations and unbound \(\upbeta\)-lactams (except for flucloxacinillin). This may be linked with a nonlinearity of protein binding, which considerably hinders the prediction of available unbound antibiotics in the blood. Additionally, a negative dependence was observed between plasma piperacillin and creatinine clearance, with a similarly low value of the Pearson Correlation Coefficient (r = \(-\)0.6337), which may be related to the small number of patients enrolled in our study. Intriguingly, no dependence at all was observed between impienem in plasma and creatinine clearance. \begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Pat. \#**} & **Sample** & **Antibiotic Treatment** & **Concentration in** & **Concentration in** \\  & **Number** & **before Sampling** & **Plasma before** & **Plasma after Infusion** \\  & & **[days]** & **Infusion [mg/L]** & **[mg/L]** \\ \hline \multicolumn{5}{c}{Patients receiving IMPENEM} \\ \hline
1 & IMI-1 & 2 & 0.918 & 15.344 \\  & IMI-2 & 4 & 1.646 & 16.283 \\  & IMI-3 & 4 & 0.756 & 8.176 \\
2 & IMI-4 & 5 & 0.293 & 17.185 \\  & IMI-5 & 4 & 1.650 & 18.394 \\
3 & IMI-6 & 5 & 0.835 & 5.627 \\  & IMI-7 & 6 & 1.157 & 12.919 \\
4 & IMI-8 & n.a. & 15.034 & 38.816 \\
5 & IMI-9 & 0 & 7.001 & 25.113 \\
6 & IMI-10 & 3 & 2.958 & 10.219 \\
7 & IMI-11 & 1 & 5.067 & 13.399 \\  & IMI-12 & 2 & 5.291 & 13.164 \\
7 & IMI-13 & 0 & 0.707 & 5.852 \\ \hline \end{tabular}
\end{table} 
Table 8: _Cont._Figure 5: Concentrations [mg/L] of piperacillin (blue, left panel) and impenem (orange, right panel) in plasma samples collected 1 h after the end of intravenous infusion in the function of antibiotic base-level before infusion (upper panel), albumins [g/L] in plasma (middle panel) and creatinine [mmol/L] in plasma (bottom panel). Tentative linear regression is given as a dotted line along with the respective value of the Pearson Correlation Coefficient.
# A Fast Acquisition Algorithm for Hybrid Signals of 5G and BeiDou B1
## 3 Results and Discussion
### 5G Out-of-Band and B1 Signal Performance Analysis
Only under some special conditions, when the base station radiates high power and is close to the base station, will the 5G signal power at the receiver be higher than the reference value. Then, BeiDou is compatible with 5G in most cases.
# On the Reliability Assessment of Artificial Neural Networks Running on AI-Oriented MPSoCs
## 3 Proposed Approach  
The proposed methodology is based on the identification of the most critical neurons inside the ANN to then determine the best scheduling of the ANN application workload in the targeted MPSoC. We assume that the ANN is ready to be deployed on the intended hardware architecture and any modifications of the ANN application are not required. Indeed, only the pretrained ANN application and the available hardware resources are considered. As experimentally demonstrated, the proposed technique is capable of increasing the reliability of the NCS. In this work, we present a methodology that is built on the following three steps:  
1. **Ranking of the criticality of single neurons**: Resilience scores are assigned to individual neurons of the ANN.
2. **Mapping and variance assignment**: Based on the previous phase and on the available PEs of the target AI-oriented MPSoC, a value is given to each chunk of neurons assigned to a single PE. We adopt a mathematical metric as a decision-making parameter--the variance. This value indicates the criticality of the chunk; in other words, the amount of critical neurons in that chunk that are assigned to a PE.
3. **ILP-based optimal scheduling**: By leveraging on the chunks variance, an ILP solver is set up to obtain the optimal reliability-oriented scheduling for mapping ANN inferences on a specific hardware device.  
These three phases are shown in Figure 1 and detailed in the following.
###### Abstract  
Indium sulfide (In\({}_{\mathrm{x}}\)S\({}_{\mathrm{y}}\))--besides CdS and Zn(O,S)--is already used as a buffer layer in chalcopyrite-type thin-film solar cells and modules. We discuss the influence of the substrate temperature during very fast magnetron sputtering of In\({}_{\mathrm{x}}\)S\({}_{\mathrm{y}}\) buffer layers on the interface formation and the performance of Cu(In,Ga)Se\({}_{\mathrm{2}}\) solar cells. The substrate temperature was increased from room temperature up to 240 \({}^{\circ}\)C, and the highest power conversion efficiencies were obtained at a temperature plateau around 200 \({}^{\circ}\)C, with the best values around 15.3%. Industrially relevant in-line co-evaporated polycrystalline Cu(In,Ga)Se\({}_{\mathrm{2}}\) absorber layers were used, which yield solar cell efficiencies of up to 17.1% in combination with a solution-grown CdS buffer. The chemical composition of the In\({}_{\mathrm{x}}\)S\({}_{\mathrm{y}}\) buffer as well as of the Cu(In,Ga)Se\({}_{\mathrm{2}}\)/In\({}_{\mathrm{x}}\)S\({}_{\mathrm{y}}\) interface was analyzed by time-of-flight secondary ion mass spectrometry. Changes from homogenous and stoichiometric In\({}_{\mathrm{2}}\)S\({}_{\mathrm{3}}\) layers deposited at RT to inhomogenous and more sulfur-rich and indium-deficient compositions for higher temperatures were observed. This finding is accompanied with a pronounced copper depletion at the Cu(In,Ga)Se\({}_{\mathrm{2}}\) absorber surface, and a sodium accumulation in the In\({}_{\mathrm{x}}\)S\({}_{\mathrm{y}}\) buffer and at the absorber/buffer interface. These last two features seem to be the origin for achieving the highest conversion efficiencies at substrate temperatures around 200 \({}^{\circ}\)C.  
Keywords: indium sulfide; sputtering; CIGS; thin film; solar cell; buffer; ToF-SIMS; depth profile +
Footnote †: journal: Applied Physics Letters