esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Signal Preprocessing  
#### 2.3.1 Distinguishing the Sub-Activities  
The Capture.U software in conjunction with the iPad offers the choice of simultaneously recording the experimental session at hand. This feature was particularly useful for distinguishing the sub-activities and finding out the critical instant, where the transition  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sensor** &
\begin{tabular}{c} **Measured** \\ **Magnitude** \\ \end{tabular} & **Range Units** & **Sensitivity** & **Axes** \\ \hline Accelerometer & Acceleration & \(\pm\)16 g & 16 bit & 3 axes \\ Gyroscope & Angular velocity & \(\pm\)2000\({}^{\circ}\)/sec & 16 bit & 3 axes \\ Magnetometer & Direction/position & \(\pm\)4900 \(\upmu\)T & 16 bit & 3 axes \\ \hline \hline \end{tabular}
\end{table}
Table 2: The specifications of the VICON IMeasureU Blue Trident sensors, as mentioned in [48].  
Figure 2: Locations of the five wearable sensors on the human body during experiments.  
between them took place similarly to [55]. Each sequence starts with the subject standing still (labeled as sub-activity \"0\") and is used as a two-fold baseline; (**a**) for establishing a distinctive and realistic \"idle\" activity and (**b**) for allowing the timely synchronization of the sensors before starting the sequence. The rest of the sub-activities are described next:  
* Walking without the crate sub-activity (labeled as \"1\") begins immediately after one of the feet first leaves the ground (beginning of stance phase of gait [56];
* The third sub-activity (labeled as \"2\") begins when the participant starts bending their trunk, kneeling, or simultaneously performs them both, which are usually referred to as stoop, squat, and semi-squat techniques, respectively, in the relative literature [57];
* The fourth sub-activity (labeled as \"3\") begins when the participant starts lifting the crate from the ground [58];
* The fifth sub-activity (labeled as \"4\") begins when the participant starts the stance phase of gait similarly to the above description, however, by carrying the crate this time;
* The sixth sub-activity (labeled as \"5\") begins when the participant starts bending their trunk, kneeling, or simultaneously performs them both, as described above, while it ends when the entire surface of the crate is placed onto the UGV.  
Obviously, as the required tasks are continuous in nature, the beginning of one task corresponds to the end of the other. Since the participants performed the tasks in their own way and at their own pace, following the above well-defined criteria was of major importance in order to assure the reliability of the results. In this fashion, it should be highlighted that in case at least one of the sensors was not synchronized with the others (this was recognized during processing the dataset), the corresponding measurements of the remaining four sensors were also discarded. The aforementioned labeling of the sub-activities was used for the rest of the signal pre-processing, as will be elaborated below.
# A Supernet-Only Framework for Federated Learning in Computationally Heterogeneous Scenarios
## 4 Experiments
### Evaluation of Supernet-Only Framework
#### 4.1.2 Evaluation of the Supernet-Only Framework Under Federated Averaging (FedAvg)
These results show that the observed improvements are not only statistically significant but also of substantial practical importance at the client level. When compared with Max Trainable, Supernet-Only also demonstrates statistically significant advantages across all datasets. Although the differences in this case are smaller than those observed against Max Runnable, they remain meaningful. All corrected \(p\)-values are below 0.01, such as \(1.89\times 10^{-3}\) on CIFAR-10. The effect sizes range from 0.45 to 0.67, which corresponds to medium-to-large practical effects. These results confirm that Supernet-Only offers both wider applicability across clients and consistently higher predictive performance. Reviewing Tables 1-3, across all different sizes of federations and federate frameworks, our method consistently outperforms the suboptimal method by more than 1% on the CIFAR-100 dataset, and this superiority exceeds 5% when using FedAvg, as shown in Tables 2 and 3. Experimental results on the CIFAR-10 and CINIC-10 datasets demonstrate that our approach consistently ranks among the top performers. Compared to other state \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Compared to** & \(W\) & \(Z\) & **Raw \(p\)** & **Corr. \(p\)** & \(r\)** \\ \hline \multirow{3}{*}{CIFAR-10} & Max Runnable & 30 & \(-7.57\) & \(1.08\times 10^{-13}\) & \(2.17\times 10^{-13}\) & 0.85 \\  & Max Trainable & 1986 & \(-5.09\) & \(9.43\times 10^{-4}\) & \(1.89\times 10^{-3}\) & 0.45 \\ \multirow{3}{*}{CIFAR-100} & Max Runnable & 159 & \(-6.94\) & \(1.41\times 10^{-11}\) & \(2.82\times 10^{-11}\) & 0.78 \\  & Max Trainable & 959 & \(-7.54\) & \(9.35\times 10^{-13}\) & \(1.87\times 10^{-12}\) & 0.67 \\ \multirow{3}{*}{CINIC-10} & Max Runnable & 8 & \(-7.68\) & \(3.30\times 10^{-14}\) & \(6.61\times 10^{-14}\) & 0.86 \\  & Max Trainable & 920 & \(-7.63\) & \(2.54\times 10^{-11}\) & \(5.08\times 10^{-11}\) & 0.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Wilcoxon signed-rank test results for client-level accuracy comparisons: This table reports statistical test results comparing the performance of our proposed method (Supernet-Only) against two baseline strategies (Max Runnable and Max Trainable) across three datasets. Reported metrics include the Wilcoxon test statistic \(W\), standardized statistic \(Z\), raw \(p\)-value, Bonferroni-corrected \(p\)-value, and effect size \(r\). Significant differences (\(p<0.01\)) and medium-to-large effect sizes (\(r\geq 0.45\)) indicate meaningful performance differences at the client level. of-the-art methods, our approach either maintains a certain advantage or only shows a minimal disadvantage, regardless of the performance changes in these methods. This indicates that our method is highly competitive and robust.
## 1 Introduction
### Related Work  
Existing PPDT protocols can be broadly classified based on the cryptographic primitives they employ.  
**HE-based protocols:**: HE supports computation over encrypted data without requiring decryption. [PERSON] et al. [5] used low-degree polynomial approximations to support non-interactive inference with communication costs independent of tree depth. [PERSON] et al. [6], [PERSON] et al. [7], and [PERSON] et al. [8] adopted the TFHE, BFV, and CKKS schemes, respectively, for efficient computation with low multiplicative depth. [PERSON] et al. [9] reported ciphertext size comparisons and proposed homomorphic traversal algorithms across various commonly used HE schemes. Most existing HE-based PPDT protocols are designed for two-party settings, similar to the linear-function-based scheme proposed by [PERSON] et al. [10].
**Protocols based on other cryptographic primitives:**: [PERSON] et al. [11] employed additive secret sharing in a two-server setting, ensuring that no single party gained access to both the model and the data, while maintaining low communication overhead. MPC-based approaches, such as those reported b [PERSON] et al. [12], combine additive HE with oblivious transfer (OT), offering strong privacy guarantees but often suffering from scalability issues, particularly with deep trees. Differential privacy (DP), although more commonly used during training [13], can complement cryptographic inference methods by adding noise to outputs to mask sensitive patterns. However, DP typically compromises utility and operates orthogonally to encrypted inference techniques.  
Within this landscape, several notable HE-based PPDT approaches stand out. [PERSON] et al. [10] proposed a prediction protocol using linear functions and DGK-based integer comparison [14], reducing complexity at the cost of requiring bitwise encryption. [PERSON] et al. [15] enhanced efficiency via the XCMP protocol, based on Ring-LWE SHE, although it struggled with large input bit lengths and unstable ciphertexts. [PERSON] and [PERSON] [16] addressed this with SK17, encoding integer bits as polynomial coefficients to support larger comparisons. [PERSON] et al. [17] further improved SK17 by introducing a faster comparison protocol and a non-interactive variant with reduced ciphertext functionality.  
Despite these advancements, existing protocols still face challenges in simultaneously achieving high efficiency, scalability, low communication overhead, and full tree structure confidentiality--especially under realistic semi-honest adversary models.
# Parallelization of the Bison Algorithm Applied to Data Classification
## 4 Experimental Setup
### Performance Metrics  
The performance of the algorithm was assessed using the following metrics:  
Accuracy: The proportion of correct predictions among the total number of examples. Precision: The proportion of true positive predictions among the total positive predictions. Recall: The proportion of true positive predictions among the total actual positives. F1-score: The harmonic mean of precision and recall, providing a single metric that balances both concerns.  
In addition, the speedup measures the parallelization ability of the algorithm by taking the ratio of the running time on a single node to the running time on parallel nodes \(n\). The speedup is calculated as follows, where the dataset size is fixed while the number of nodes is increased in a certain ratio.  
\[Speedup=\frac{T_{1}}{T_{n}}, \tag{1}\]  
where \(T_{1}\) is the running time using a single node, and \(T_{n}\) is the running time using \(n\) nodes.  
The scaleup measures how the cluster of nodes are utilized efficiently by the parallel algorithm. The scaleup is calculated as follows, where the dataset size and the number of nodes are increased by the same ratio.  
\[Scaleup=\frac{T_{sn}}{T_{Rsn}}, \tag{2}\]  
where \(T_{sn}\) is the running time for the dataset with size \(s\) using \(n\) nodes, \(R\) is the increasing ratio, and \(T_{Rsn}\) is the running time for the dataset with size \(Rs\) using \(Rn\) nodes.
## 2 Materials and Methods
### Fluorescence Spectroscopy  
From the pooled samples which were described in Section 2.1, \"Sample collection and preparation\", six spots of 2 \(\mu\)L sample were pipetted on a thin layer chromatography plate (Silica gel 60, Merck, Germany) as fluorescent measurements were obtained at six different times after deposition. Fluorescence measurements were acquired using a LS55 Luminescence spectrometer equipped with a fiber optic accessory (Perkin Elmer, Waltham, MA, USA). To obtain the protein fluorescence, the samples were excited at 283 nm, while the emission was recorded at 313 to 550 nm. The fluorescence of the FOX was recorded at an excitation wavelength of 365 nm, in the emission wavelength range from 400-500 nm. For measuring semen, the (physical) slit width in the light path at the excitation and emission side of the LS55 resulted in spectral resolutions of 10 and 7.5 nm, respectively. For urine, both the excitation and emission spectral bandwidth were 15 nm, while for saliva, they were 10 and 5 nm, respectively. Data analysis included background correction and the calculation of the protein/FOX ratios by dividing the average maximum emission intensities of these spectra using Matlab software R2023b. For semen and saliva, the fluorescence intensity at 340 nm was divided by the fluorescence intensity at 430 nm. For urine, the fluorescence intensity at 370 nm was divided by the fluorescence intensity at 430 nm. The protein/FOX ratios were calculated for every time point of each time series. For one urine donor and two saliva donors, the samples were excluded due to very low FOX levels.
# An Experimental Study on D2D Route Selection Mechanism in 5G Scenarios
## 1 Introduction
The source node has its data stream information (i.e., the format and size of data) and receives network-wide information, including the available routes, as well as the hop count and the number of white spaces of each route, from CC through MC BS. The source node makes route selection, taking into account: (a) the number of white spaces, which are random in nature, along with given routes ranked by CC; and (b) the hop count of the route. A route with lower hop count and higher number of white spaces is ranked higher. To improve the accuracy of network performance measurement, we have chosen to use our testbed. In traditional testbeds, all USRP/GNU radio nodes are connected to a single processing unit (e.g., a personal computer, PC) via a switch using cables, and so there are three assumptions made in the experiment: (a) control messages are exchanged via the wired cables and data packets are exchanged via the wireless medium; (b) a single processing unit makes decisions in a centralized and homogeneous manner [15]; and (c) the nodes are placed close to one another due to the constraint of the wired cable. The assumptions affect the measurement of network performance, particularly end-to-end delay and packet loss ratio. Our testbed is different from the traditional testbed, whereby each node is connected to separate processing units (i.e., RP3), and so it provides three main advantages: (a) control messages and data packets are exchanged via the wireless medium; (b) separate processing units make decisions in a distributed and heterogeneous manner (e.g., separate processing units with different processing capability); and (c) the nodes are placed further apart from one another. Therefore, the response delay of control message exchange and the packet loss caused by the operating environment (e.g., ambient noise) are not ignored in our end-to-end delay and packet delivery ratio measurement. Network performance measures, including the amount of traffic offload from the MC to SCs, as well as the packet delivery ratio and end-to-end delay of D2D routes and the backbone route, provide insights on whether traffic offload using D2D is preferred in route selection.
# An Adaptive and Secure Holographic Image Watermarking Scheme
## 3 Encrypted Binary Computer-Generated Hologram Based on Chaos
### The Image Permutation Method Using SLMM and Logistic Map  
Assuming that the size of the input image \(I(x,y)\) is \(M\times N\), the scrambling method is described as the following steps:  
1. Initialize \(SX(1)\), \(SY(1)\) and \(SZ\) which are between 0 and 1 randomly and choose an arbitrary natural number \(T\) first. Then with \(SX(1)\) and \(SY(1)\), iteratively generate two chaotic sequences \(SX(i)\) and \(SY(i)\) using Equation (3). The lengths of \(SX(i)\) and \(SY(i)\) both are \(MN+T\). Here, \(i=1,2\),...,\(MN+T\).
2. Generate two random integers \(t1\) and \(t2\) between 1 and \(MN+T\). Then calculate the initial value \(XL(1)\) of logistic map according to the following Equation (6) \[XL(1)=\big{\{}\big{|}[SX(t1)+SY(t2)]/2\big{|}-SZ\big{|}/10,\] (6) where \(SX(t1)\) and \(SY(t2)\) are the \(t1^{\text{th}}\) element in \(SX\) and the \(t2^{\text{th}}\) element in \(SY\), respectively.
3. Using \(XL(1)\) and Equation (4), generate the chaotic sequences \(XL(i)\) whose length is \(MN+T\) iteratively. Here, \(i=1,2,\)...,\(MN+T\).
4. Generate a random integer \(t3\) between 1 and \(T\). Truncate \(NM\) elements of \(XL(i)\) from the \(t3^{\text{th}}\) element to obtain a chaotic sequence \(SE=\{XL(i)\), \(i=t3,t3+1,\)...,\(t3+MN-1\}\).
5. Subsequently, a new sequence \(SP\) and its corresponding permutation indices \(ISP\) can be obtained by sorting the sequences \(SE\) in ascending order. There are \(MN\) elements in \(ISP\). The relations between \(SE\) and \(SP\) is \(SP\)=\(SE(ISP)\). For example, the \(m^{\text{th}}\) element in \(SP\) corresponds to the \(ISP(m)^{\text{th}}\) element in \(SE\).
6. Map \(I(x,y)\) into a 1D array \(IZ\) by use of the zigzag algorithm [24]. The length of \(I1\) is \(MN\).
7. Then the permutation indices \(ISP\) is utilized to permute \(IZ\) and the scrambled vector \(IV\) can be achieved as follows \[IV=IZ(ISP).\] (7)
8. Finally, the permuted image \(SEI\) can be achieved by applying the inverse zigzag scan process [24] to \(IV\).  
The inverse image permutation process is similar to the image permutation process. In inverse scrambling process, as described in steps (1)-(5), the permutation indices \(ISP\) is achieved first using the same initial values and control parameters of the chaotic functions. Then the permuted image \(SEI\) is mapped into a 1D vector \(SEI1\) by employing the zigzag algorithm. Subsequently, permute \(SEI1\) back to their original position according to the following equation  
\[DSI(ISP)=SEI1. \tag{8}\]  
Finally, apply the inverse zigzag algorithm to \(DSI\) to retrieve the decrypted image \(DI\). The parameters \(SX(1)\), \(SY(1)\), \(SZ\), \(\alpha\), \(\beta\), \(\gamma\), \(t1\), \(t2\) and \(t3\) are employed as private (secret) keys.
## 2 Materials and Methods
### Quality Assurance  
Quality assurance for the sediment samples involved the use of certified reference materials (GBW07312) provided by the Institute of Geophysical and Geochemical Exploration, Chinese Academy of Geological Sciences [26]. To ensure accuracy, analytical reagent blanks were incorporated with every batch of digested samples and analyzed in the same manner to account for background interference. Each sample underwent triplicate measurements to evaluate both precision and accuracy. Results for the reference materials exhibited variabil  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & **GPS** & **Landmark** \\ \hline
1 & 32\({}^{\circ}\)14\({}^{\prime}\)55\({}^{\prime\prime}\) N 119\({}^{\circ}\)02\({}^{\prime}\)57\({}^{\prime\prime}\) E & Branching off from the Yangtze River \\ \hline
2 & 32\({}^{\circ}\)15\({}^{\prime}\)19\({}^{\prime\prime}\) N 119\({}^{\circ}\)01\({}^{\prime}\)48\({}^{\prime\prime}\) E & A pipe connecting to agricultural discharge is seen. \\ \hline
3 & 32\({}^{\circ}\)14\({}^{\prime}\)59\({}^{\prime\prime}\) N 119\({}^{\circ}\)00\({}^{\prime}\)32\({}^{\prime\prime}\) E & Normal \\ \hline
4 & 32\({}^{\circ}\)15\({}^{\prime}\)18\({}^{\prime\prime}\) N 119\({}^{\circ}\)59\({}^{\prime}\)23\({}^{\prime\prime}\) E & A pipe connecting to agricultural discharge is seen. \\ \hline
5 & 32\({}^{\circ}\)15\({}^{\prime}\)08\({}^{\prime\prime}\) N 119\({}^{\circ}\)57\({}^{\prime}\)32\({}^{\prime\prime}\) E & Normal \\ \hline
6 & 32\({}^{\circ}\)14\({}^{\prime}\)55\({}^{\prime\prime}\) N 119\({}^{\circ}\)56\({}^{\prime}\)02\({}^{\prime\prime}\) E & A pipe connecting to agricultural discharge is seen. \\ \hline
7 & 32\({}^{\circ}\)14\({}^{\prime}\)31\({}^{\prime\prime}\) N 119\({}^{\circ}\)54\({}^{\prime}\)57\({}^{\prime\prime}\) E & Normal \\ \hline
8 & 32\({}^{\circ}\)13\({}^{\prime}\)36\({}^{\prime\prime}\) N 118\({}^{\circ}\)55\({}^{\prime}\)20\({}^{\prime\prime}\) E & Closer to residential communities, exposed to household discharge. \\ \hline
9 & 32\({}^{\circ}\)12\({}^{\prime}\)33\({}^{\prime\prime}\) N 118\({}^{\circ}\)55\({}^{\prime}\)38\({}^{\prime\prime}\) E & Closer to residential communities, exposed to household discharge. \\ \hline
10 & 32\({}^{\circ}\)11\({}^{\prime}\)59\({}^{\prime\prime}\) N 119\({}^{\circ}\)56\({}^{\prime}\)17\({}^{\prime\prime}\) E & Reconnecting with Yangtze River. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sampling sites and the landmarks associated with them.  
ity within 10%, and the relative standard deviations (RSDs) for triplicate samples remained under 10%. The detection limits for various elements were as follows: 0.01 mg/Kg for Cd, 0.05 mg/Kg for Cu, 0.1 mg/Kg for Pb, 0.2 mg/Kg for Zn, 0.4 mg/Kg for Ni, 0.001 mg/Kg for Hg and As, and 0.25 mg/Kg for Cr (VI). Recovery rates for Cd, Cr (VI), Cu, Pb, Zn, Ni, Hg, and As ranged from 97% to 102.3%, 95.2% to 103.7%, 97.2% to 102.8%, 95.1% to 105.7%, 91.2% to 102.4%, 98.3% to 105.8%, 94.3% to 104.6%, and 95.4 to 104.9%, respectively.
# Modeling and Spatial Diversity-Based Receiving Improvement of In-Flight UAV FSO Communication Links
## 3 Performance Analysis of the In-Flight UAV FSO Communication Link
### Transmission Analysis of the FSO Communication for UAV during Yaw Flight  
The optical communication process of the receiving-end UAV in the state of yawing flight is simulated by setting \(\theta_{FOV}\) = 40 mrad, \(\sigma_{rp}^{2}=\sigma_{lp}^{2}=1\) cm, \(\sigma_{ro}^{2}=\sigma_{lo}^{2}=1\) mrad. However, the fuselage still has an angle to the \(y\) axis while the UAV is yawing. Therefore, this simulation sets the tilt angle \(\varphi\) as 5 mrad, and the variance of the random angle deflection around the \(y\) axis \(\sigma_{a}^{2}=0\) mrad, 1 mrad, 2 mrad, and 5 mrad, respectively.  
Figure 6 shows the distribution of the FSO communication channel coefficient under different fuselage tilt angles when the UAV flies in yaw motion on the same horizontal plane. As the wind resistance increases, the swing range of the UAV around the \(y\) axis becomes larger. The value of the UAV FSO communication channel coefficient \(h\) gradually becomes smaller, which means that the quality of the optical communication is getting worse.  
As can be seen from Figure 7, the influence of UAVs' jitter caused by the wind resistance on its communication quality is less than that of the tilt angle of the fuselage. In Figure 7a, with the increase of the random wind resistance, the outage probability of the UAVs' optical communication slightly increases. Under the condition that the SNR is equal to 20 dB, the outage probability of the link increases to \(10^{-3}\) when \(\sigma_{a}^{2}\) increases from 0 to 5 mrad. With the requirement of the outage probability reaching \(10^{-3}\), the SNR should be increased from 17 dB to more than 20 dB when \(\sigma_{a}^{2}\) increases from 0 to 5 mrad.  
Figure 5: FSO communication performance of UAVs at different tilt angles when the UAV flies straightly at the same altitude. (**a**) Outage probability; (**b**) average BER.  
In Figure 7b, the BER will also increase when \(\sigma_{\alpha}^{2}\) increases. At the SNR of 20 dB, the BER will increase from 3 \(\times\) 10\({}^{-6}\) to 2 \(\times\) 10\({}^{-4}\) when \(\sigma_{\alpha}^{2}\) increases from 0 to 5 mrad. The effect of the horizontal random wind resistance on the UAV FSO communication is smaller than the influence of the tilt angle. With the requirement of the average BER reaching 10\({}^{-5}\), the SNR should be 15 dB, 17 dB, and 20 dB when \(\sigma_{\alpha}^{2}\) is 0 mrad, 1 mrad, and 2 mrad, respectively. However, when \(\sigma_{\alpha}^{2}\) is 5 mrad, the average BER is still higher than 10\({}^{-4}\) even if the SNR reaches 20 dB. In this case, it is considered that the communication quality between UAVs is slightly lower than the critical point of effective communication. Although communication can be established, the link is relatively unstable.