esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Measurement Principles and Device
### Composite Uncertainty  
In summary, the main factors affecting the uncertainty of emissivity measurement are shown in Table 1. Due to the progress of the experiment, the uncertainties of other items have not yet been fully measured. Based on empirical data, assumed values were assigned to them as in Table 1.  
Due to the complexity of the relationship between the combined uncertainty and its individual components, it is difficult to establish a simple model. Therefore, the method of summing squares is used for combination:  
\[u=\sqrt{u_{1}^{2}+u_{2}^{2}+u_{3}^{2}+u_{4}^{2}+u_{5}^{2}+u_{6}^{2}+u_{7}^{2}} \tag{17}\]  
The final uncertainty of the emissivity measurement is obtained as shown in Table 2:  
\begin{table}
\begin{tabular}{c c c} \hline
**Item.** & **Source of Uncertainty** & **Standard Relative Uncertainty** \\ \hline \(u_{1}\) & Reference Blackbody Source & 0.03\% \\ \(u_{2}\) & Temperature & \(2\times 10^{-6}\) \\ \(u_{3}\) & Repeatability & 0.07–0.56\% \\ \(u_{4}\) & Stability & 0.15–0.98\% \\ \(u_{5}\) & System Linearity & 0.23\% \\ \(u_{6}\) & Wavelength & 0.01\% \\ \(u_{7}\) & Spectral Resolution & 0.01\% \\ \hline \end{tabular}
\end{table}
Table 1: Summary table of emissivity uncertainty.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Wavelength (\(\upmu\)m)** & **0**\({}^{\circ}\)**C** & **20**\({}^{\circ}\)**C** & **40**\({}^{\circ}\)**C** & **60**\({}^{\circ}\)**C** & **80**\({}^{\circ}\)**C** & **100**\({}^{\circ}\)**C** \\ \hline
4.0 & 0.74\% & 1.05\% & 0.76\% & 0.51\% & 0.68\% & 0.87\% \\
4.5 & 0.55\% & 0.77\% & 0.78\% & 0.46\% & 0.79\% & 0.74\% \\
5.0 & 0.85\% & 0.91\% & 0.76\% & 0.53\% & 0.54\% & 0.59\% \\
5.5 & 0.61\% & 0.80\% & 0.55\% & 0.37\% & 0.52\% & 0.63\% \\
6.0 & 0.90\% & 1.18\% & 0.73\% & 0.56\% & 0.76\% & 0.72\% \\
6.5 & 1.42\% & 1.50\% & 1.02\% & 0.77\% & 0.99\% & 0.81\% \\ \hline \end{tabular}
\end{table}
Table 2: Total uncertainty of emissivity measurement.  
Figure 9: Results of measuring linearity with double-aperture method.
## 5 Common Systems
### Timing and Synchronization
#### 5.1.5 Client Requirements  
The clients of the ultra-stable timing reference generation and distribution system are to date: High power RF via LLRF, Bunch Arrival Monitor (BAM), Laser Arrival Monitor (LAM), Gun and Experimental Lasers to be synchronized to the reference, diagnostic monitors all along the machine, Event timing system, and switch-yard kicker.  
The stability requirements concerning reference signal drift and integrated jitter depend very much on the various clients. Therefore distribution sub-systems based on adapted technologies and adequate performance were either developed or purchased with an emphasis on an optimized ratio of performance to investment in equipment. The types of links and the final performance goals are listed in Table 5. In the following sections the technologies of the different links and their realization/installation status is described in more detail.
# Measurement of Sub-Surface Microstructures Based on a Developed Ultrasonic Atomic Force Microscopy
## 2 Materials and Methods
### Equipment and Sample  
A new prototype CR-AFM equipment built according to the schematic of Figure 1 is shown in Figure 3. It can be seen from the enlarged view that a piezoresistive cantilever probe (PRSA-L400-F30-Si-PCB, SCL-SENSOR) is attached to the piezoelectric actuator through a connector. The piezoelectric actuator has a stroke of 30 \(\upmu\)m and a positioning resolution of 0.6 nm. Then, the contact resonant frequency of the probe and the sample is found by contacting the probe with the sample and sweeping the frequency, then an ultrasonic excitation near this frequency is applied to the probe cantilever by a peripheral signal generator. The property parameters of the cantilever probe are shown in Table 1.  
The preparation process of the samples with subsurface microstructure for experiments is as follows. First, a grating with a width of 2 \(\upmu\)m, a depth of 500 nm and a period of 4 \(\upmu\)m is etched on a silicon wafer. The HOPG film is then transferred to the photolithographic silicon wafer by mechanical stripping, and under the action of van der Waals force, HOPG can be well combined with the surface of the silicon wafer [27; 28]. Finally, AFM and white light interferometer are used to detect the thickness of the pasted HOPG film.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Tip Material** & **Tip Radius** & **Tip Height** & **Resonant Frequency** \\ \hline Si & \textless{}15 nm & 4.5–4.6 \(\upmu\)m & 30 kHz \\ \hline \hline \end{tabular}
\end{table}
Table 1: Property parameters of the probe.  
Figure 3: The prototype CR-AFM system used in experiments.
## 3 Results
### Elements Content  
The determined heavy metals and additional elements concentrations were compared with the last Czech state-wide survey [27; 90] and data from similar surveys in Spain (Zumarraga, Azkoitia) [37] and Turkey (Eregli) [35]--see Table 2. While comparing the data with these studies, it should be noted that the Czech state-wide survey was realised in 2010, the Turkish in 2008 and the Spanish in 2007, and since then, the industrial emission has decreased due to various adopted measures [4; 10] (see Figure 2). Moreover, the predominant moss species within the Czech survey were _Pleurozium schreberi_ and _Scleropodium purum_, while the Spanish and Turkish studies were based on the collection of _Hypumum cu-pressforme_ (Hedw.). Though it is supposed that elements concentrations in moss tissue are species-dependent [56; 91; 92], the information on the interspecies calibration for concerned moss species is still lacking; thus, it could not be performed.  
The comparison presented in Table 2 shows that levels of all elements recorded in Trince were significantly higher in both June and October than in the Czech state-wide data, with the exception of Mn. The most important difference was in the case of Cr, Sb (June) and Cd (an order of magnitude difference), and subsequently, it decreased in the order Ni \(>\) Al \(>\) V \(>\) Zn \(>\) Fe \(>\) As \(>\) Hg \(>\) Cu. Increased concentrations of the listed elements were also stated for the region in previous biomoritoring studies [93; 94]. The median value of elements concentration decreased across the sampling area in the order Fe \(>\) Al \(>\) Mn \(>\) Zn \(>\) Cr \(>\) Pb \(>\) Cu \(>\) Ni \(>\) V \(<\) Cd \(>\) Sb \(>\) As and Hg \(>\) Al \(>\) Mn \(>\) Zn \(>\) Pb \(>\) C \(>\) Cu \(>\) V \(>\) Ni \(>\) Cd and Hg in October. Within the Czech state-wide survey, it was Fe \(>\) Mn \(>\) Al \(>\) Zn \(>\) Cu \(>\) Pb \(>\) V \(>\) Ni \(>\) Cr \(>\) As \(>\) Cd \(>\) Sb and Hg. On the other hand, the medians in Trince do not reach the values of the Spanish study (except for Hg). The concentrations of Fe, Cr and Cu in Trince are about twice higher than the concentrations recorded in Turkey; those of Ni and Pb are rather comparable.  
From Table 2, it is also apparent that June concentrations across the area were higher in the case of Al, Mn, Fe, Ni, Cu, Zn, As and Sb; October concentrations were higher for V, Cr, Cd, Hg, Pb and N. This can be attributed to wet deposition as snowfalls in the area are acidic, thus increasing the accessibility of metals for plants, and to the different emission load related to heating during the winter season.
# Outage Probability Analysis in Relaying Cooperative Systems with NOMA Considering Power Splitting
## 2 System Model
Thus, following from (4) and (5), the SNRs for \(x_{1}\) at both \(R\) and \(D\) can be respectively obtained as  
\[\gamma_{R,1,x_{1}}=\frac{\beta(1-\rho)|f_{1}|^{2}}{d_{SR}^{m}}, \tag{6a}\]  
Figure 2: Energy harvesting and information transmission protocol. Figure 2.0  
where the transmit SNR is written as \(\beta=P_{S}/N_{0}\). In this system model, \(D\) receives signals from \(S\) while using successive interference cancellation (SIC) to conduct successful signal decoding. Therefore, we compute the received signal at \(D\) as  
Figure 2.1  
Replacing (3) into (7), we can rewrite (7) as  
Figure 2.2  
In fact, due to the different locations wireless nodes, the fading gain of the \(R\)-\(D\) channel, \(f_{2}\) is proved to be more significant than that of the \(S\)-\(D\) channel, \(f_{3}\). Due to the natural characteristics of different transceivers' channels, it motivates us to apply NOMA in the second time slot. Thanks to the deployment of SIC, \(D\) treats \(x_{1}\) while considering \(x_{2}\) as a noise term. As a consequence, we have to remove \(x_{1}\) from \(y_{D,2}\) to successfully decode \(x_{2}\). Hence, the received SNRs at \(D\) for both data symbols can be obtained as  
Figure 2.3 (9a) and Figure 2.4  
Thanks to the use of R, the end-to-end SNR for \(x_{1}\) can be obtained as  
Figure 2.5  
The data rate the end-to-end SNR at \(D\) for \(x_{1}\) and both \(x_{1}\) and \(x_{2}\) can be written with fixed DF relaying scheme as  
Figure 2.6 (11a) and Figure 2.7  
where we defined \(\gamma_{\ell 2x,x_{1}}\), \(\gamma_{D,1,x_{1}}\) above.
### Other Existing Works and Key Contributions of Proposed Technique
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Other Techniques** & \multicolumn{2}{c}{**Key Aspects of Other Techniques**} & \multicolumn{2}{c}{**Differences from Our Proposed Technique**} \\ \hline \hline
[12] [PERSON]. [PERSON] et al. \"Deep neural network method for channel estimation in visible light communication\" & * Uses CNN/DNN-based channel estimation to mitigate channel impairments in VLC. * Focuses on accurate channel response prediction and equalization. * Emphasizes re-creating the original signal without compressing it. & Our Approach: We deliberately mask (delete) large chunks of the DMT waveform, then employ MAE to reconstruct it at the receiver, thereby maximizing data throughput. * Prior works assume the original waveform is fully transmitted; by contrast, we send only a partial signal. * We also leverage multivariate time-series patch structures, using global context to fill in missing data, which diverges from straightforward channel estimation. & Our Approach: Focuses on indoor Li-Fi over 1 m rather than underwater channels and uses MAE-based compression at up to 85\% masking. * We do not merely “compensate for channel distortions”. Instead, we reduce the time-domain signal length (i.e., actual sample count) while preserving data integrity. This leads to a 15\(\times\) capacity gain within the same 10 MHz bandwidth. & * Our Approach: Implements time-domain compression by adjusting the “masking ratio” (up to 75-85\%), so that only a fraction of patches is sent. * We perform 1-D convolution + positional encoding on subdivided “patches”, enabling the MAE decoder to reconstruct missing sections based on global self-attention. * Compared to CNN-based end-to-end solutions, our MAE can handle high masking ratios and still yield good EVM performance. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._deep learning-based optical enhancements. This leads to a true increase in data throughput for Li-Fi systems, corroborated by real-world experiments. We emphasize once more that this \"partial signal transmission + global context reconstruction\" is a novel concept enabling substantial capacity gains under standard indoor lighting conditions.
# Application of RFID to Soil-Erosion Research
## 2 Materials and Methods
Although the goal of this experiment was to test the range and accuracy of remote detection of the tagged particles, so that their size, shape and density in relation to the surrounding gravel is not important, we made them similar to comparably sized quartz particles for possible use in further experiments. The reader employed was a UHF band IC tag handy reader writer XIT-261-G manufactured by Welcat Inc. Tokyo, Japan. Trials with the RFID reader showed that the tags could be read from a distance of about 100 mm. Within the plot used for the first experiment, we constructed a 1-m-wide plot along the centre line (Figure 2). The cross section of the plot had been previously shaped into a shallow depression (maximum depth 100 mm) running the full length of the plot (less the top 50 cm) to achieve a range of runoff depths. The particles were placed at 50 mm spacings in a 5-across by 20-down array starting 6.4 m from the top of the plot. The array of particles was located such that the deepest part of the depression corresponded with the fourth line of the array. We established a grid over the area of the tagged particles and sited the RFID reader at 25-mm spacings in a rectangular grid, recording all the RFIDs that could be detected from each location (Figure 5). The plot was then subject to 22 min of rainfall at 200 mm/h. We used a higher rainfall intensity in this experiment in order to achieve greater runoff, and longer travel distances of the particles than occurred in the first experiment. Runoff was collected volumetrically in 10-s samples taken at 1-min intervals. Tagged particles leaving the plot were collected with, and between, the runoff samples, and those remaining on the plot were identified after the experiment using the same grid to locate the reader. Figure 3: An artificial gravel particle used in the first experiment shown within the Radio-Frequency Identification reader.
## 6 Analysis of Scan Patterns Dimensions: Rules-of-Thumb for Scanner Designs
### FOV of Scanners with a Pair of Rotational Risley Prisms
#### 6.1.4 Study with Regard to the Distance \(L\) from the Scanner to the Target  
The solution to increase FOV by increasing \(L\) may be applicable only in some situations, as in general the magnitude of \(L\) is imposed by the application itself. For Remote Sensing or for detection in Security and Defense, for example, \(L\) is large, and the simple \(2 LD_{lin}^{max}\) relationship can be applied for the radius of the FOV, neglecting the small terms of \(y_{jp}^{max},j=a,b,c,d\) deduced in Section 3 (Figure 7). For other applications, such as endoscopy (but also for more common setups, with \(1^{\prime\prime}\) diameter prisms), \(L\) can be comparable to the diameter of the prisms, \(2b\), and the dimensions of the FOV must be calculated taking into account the exact values of the maximum linear deviations (Equation (8)).  
An interesting case is represented by applications that have intermediate values of \(L\), of up to (or around) \(1\) m, as considered in all the studies in Figures 4-6, as well as in Figures 8-10. For such situations (in optical metrology, laser manufacturing, 3D printing, etc.) and especially when the distance from the scanner to the target plane is kept constant, \(L\) can be increased (as pointed out for the dimension \(e\) above) without increasing the dimensions of the system by using three or even only two supplementary mirrors [64]. Thus, a supplemental increase of the FOV can be obtained.
## 4 Experimental Results
### Receiver Operating Characteristic Curves  
Figures 8 and 9 illustrate the ROC curves for the proposed EDADS-1 and EDADS-2, respectively, employing the standard IEEE 14-, 39-, 57- and 118-bus systems for testing. The ROC curve is obtained by plotting the false positive rate (FPR) versus the true positive rate (TPR). FPR is defined as the probability that normal data are identified as compromised. It is used as a measure of specificity in our detection scheme. The sensitivity of our scheme is defined as the probability that compromised data are identified as assaulted. TPR is used as a measure of sensitivity. From Figures 8 and 9, we can see that the area under the curve is closer to 1 one in all cases. This means that the detection accuracy of the proposed schemes is near one, which validates its good performance. In the next subsections, we elaborate on the accuracy and \(F_{1}\) score for the proposed schemes.  
Figure 6: Three-dimensional representation of the proposed EDADS-1 with three features for standard IEEE 14- and 39-bus systems. (**a**) IEEE 14-bus system; (**b**) IEEE 39-bus system.  
Figure 7: Three-dimensional representation of the proposed EDADS-2 with three features for standard IEEE 57- and 118-bus systems. (**a**) IEEE 57-bus system; (**b**) IEEE 118-bus system.
###### Abstract  
In centralized massive multiple-input multiple-output (MIMO) systems, the channel hardening phenomenon can occur, in which the channel behaves as almost fully deterministic as the number of antennas increases. Nevertheless, in a cell-free massive MIMO system, the channel is less deterministic. In this paper, we propose using instantaneous channel state information (CSI) instead of statistical CSI to obtain the power control coefficient in cell-free massive MIMO. Access points (APs) and user equipment (UE) have sufficient time to obtain instantaneous CSI in a slowly time-varying channel environment. We derive the achievable downlink rate under instantaneous CSI for frequency division duplex (FDD) cell-free massive MIMO systems and apply the results to the power control coefficients. For FDD systems, quantized channel coefficients are proposed to reduce feedback overhead. The simulation results show that the spectral efficiency performance when using instantaneous CSI is approximately three times higher than that achieved using statistical CSI.  
cell-free massive MIMO; FDD; conjugate beamforming; downlink transmission; power control +
Footnote †: 2021: 23, 1552. [[https://doi.org/10.3390/e23111552](https://doi.org/10.3390/e23111552)]([https://doi.org/10.3390/e23111552](https://doi.org/10.3390/e23111552))
# In-Vivo Quantification of Knee Deep-Flexion in Physiological Loading Condition trough Dynamic MRI
###### Abstract  
The in-vivo quantification of knee motion in physiological loading conditions is paramount for the understanding of the joint's natural behavior and the comprehension of articular disorders. Dynamic MRI (DMRI) represents an emerging technology that makes it possible to investigate the functional interaction among all the joint tissues without risks for the patient. However, traditional MRI scanners normally offer a reduced space of motion, and complex apparatus are needed to load the articulation, due to the horizontal orientation of the scanning bed. In this study, we present an experimental and computational procedure that combines an open, weight-bearing MRI scanner with an original registration algorithm to reconstruct the three-dimensional kinematics of the knee from DMRI, thus allowing the investigation of knee deep-flexion under physiological loads in space. To improve the accuracy of the procedure, an MR-compatible rig has been developed to guide the knee flexion of the patient. We tested the procedure on three volunteers. The overall rotational and positional accuracy achieved are 1.8\({}^{\circ}\)\(\pm\) 1.4 and 1.2 mm \(\pm\) 0.8, respectively, and they are sufficient for the characterization of the joint behavior under load.  
Dynamic MRI; weight-bearing MRI; knee deep-flexion Article
## 3 Model Structure
### Auto-Correlation Mechanism and Correction Module  
#### 3.2.1 Auto-Correlation Mechanism  
We are inspired to calculate the similarity of query and key by the following formula based on the use of Fourier transform in autoformer [30].  
\[R_{Q,K}=QK. \tag{3}\]  
The \(R_{Q,K}\) represents the similarity \(\{Q\}\) to sequence \(\{K\}\) as the weight for calculating attention.  
As shown in Figure 2, the auto-correlation mechanism [30] uses sequences with high similarity to calculate auto-correlation, discarding sequences with poor similarity, which reduces the computational effort while ensuring the computational accuracy. The time  
Figure 1: VMD algorithm flow chart.  
delay aggregation block is the core of the auto-correlation mechanism, and the detailed steps in the single-head auto-correlation mechanism are as follows.  
\[\begin{split}\tau_{1},\ldots,\tau_{k}&=\arg\text{ Topk}\bigg{(}R_{Q,K}(\tau)\bigg{)},\\ \tau\epsilon\{1,\ldots,L\}\\ \widehat{R}_{Q,K}(\tau_{1}),\ldots,\widehat{R}_{Q,K}(\tau_{k})& =\text{SoftMax}\bigg{(}R_{Q,K}(\tau_{1}),\ldots,R_{Q,K}(\tau_{k}) \bigg{)},\\ \text{Auto}-\text{Correlation}(Q,K,V)&=\sum_{i=1}^{k }\text{Roll}(V,\tau_{i})\widehat{R}_{Q,K}(\tau_{i}).\end{split} \tag{4}\]  
In the above equation, L represents the sequence length of key and value, k = 10 c, \(\widehat{R}_{Q,K}\) is the auto correlation of \(R_{Q,K}\) after the operation of SoftMax normalization. Roll(V,\(\tau_{i}\)) serves to delay the value of V by \(\tau_{i}\) steps.  
The multi-head auto-correlation mechanism can parallelize the operations of the single-head auto-correlation mechanism and play a better prediction effect, and we use the multi-head auto-correlation mechanism with the following formula. In the multi-head auto-correlation mechanism, \(W_{\text{output}}\) is a linear mapping parameter, the number of channels of the hidden variable is d_model, the number of heads is h, and the query, key, and value values of the i-th head are \(Q_{i},K_{i},V_{i}\epsilon\mathbb{R}^{L\times\frac{d\text{model}}{h}},i\in\{1, \ldots,h\}\).  
\[\begin{split}\text{Multihead}(Q,K,V)&=W_{\text{ output}}*\text{Concat}(\text{head}_{1},\ldots,\text{head}_{h}),\\ \text{where head}_{i}&=\text{Auto}-\text{Correlation}(Q _{i},K_{i},V_{i}).\end{split} \tag{5}\]
# A Two-Phase Approach for Single Container Loading with Weakly Heterogeneous Boxes
## 3 Computational Experiments
### Numerical Results  
To evaluate the potentialities behind our BLHM algorithm, computational experiments were performed over difficult benchmark instances proposed by [PERSON] and [PERSON] [6], which can be accessed via OR-Library [35]. Instances can be either weakly heterogeneous (few types of boxes, each one with many units available) or strongly heterogeneous (many types of boxes, each one with few units available). We decided to conduct our analysis only on weakly-heterogeneous datasets BR1 and BR2, which contain three and five types of boxes, respectively, after preliminary tests showed that the required computation time would be considerably high to compete with alternative approaches for strongly-heterogeneous instances. We present results in Tables 1 and 2 (BR1) and Tables 3 and 4 (BR2). In these tables, for each instance, we present the volume utilization achieved by well-known state-of-the-art approaches enforcing orientation and stability constraints: the _Container Loading by TRee Search_ (CLTRS) of [PERSON] and [PERSON] [4], the _Iterative-Doubling-Greedy Lookahead Tree Search_ (ID-GLTS) of [PERSON] and [PERSON] [7], and the _Beam Search_ (BSG-VCS) of [PERSON], [PERSON], and [PERSON] [36] with a new heuristic function for selecting boxes. To the best of our knowledge, BSG-VCS has reported the best results to date. It is important to stress that CLTRS, ID-GLTS, and BSG-VCS have a maximum runtime of 500 s for each instance. The columns related to BLHM show, for each instance, the average (_avg_.) and the best (_best_) solution values found by our algorithm over 10 runs. We indicate by bold font the best solution found among the four CLP algorithms. To compare the algorithms, since BLHM is non-deterministic, we consider its average value (and denote by * in the case that the best solution of BLHM is better than the solutions found by the other algorithms). We begin the discussions on experimental analysis by stating that there is not a particular algorithm that stands out from the others for all instances. Taking as the reference group BR1, for instance, BLHM found new best solutions for 24 (out of 100) instances. The average solution obtained by CLTRS, ID-GLTS, BSG-VCS, and BLHM was 94.51, 94.40, 94.74, and 92.97 (considering the _average_ column), respectively. For BR1-043, the result of 83.98 obtained by BLHM was very small when compared to the best one, namely 96.18. We believe this is due to the small number of FSBs generated in the first phase of BLHM.