esa-sceva/satcom-chunk-collection huggingface dataset:
# Effective Privacy-Preserving Collection of Health Data from a User's Wearable Device
## 4 Proposed Method
### Server Processing of the Collected Data  
After receiving the perturbed salient data set from a wearable device, the data collection server first reconstructs the health data based on the perturbed salient data set. Given \(PFS_{i}\), for easy explanation, let us assume that the timestamp of a salient datum in \(PFS_{i}\) satisfies the condition \(t_{f_{1}}<t_{f_{2}}<\cdots<t_{f_{m}}\)  
Figure 3: Example of computing the error by the function EstimateError_by_LDP().  
Given two adjacent salient data, \((t_{f_{h}},px_{f_{h}})\) and \((t_{f_{h+1}},px_{f_{h+1}})\), the health data located between two timestamps, \(t_{f_{h}}\) and \(t_{f_{h+1}}\), are estimated by the points that lie on the linear line connecting \((t_{f_{h}},px_{f_{h}})\) and \((t_{f_{h+1}},px_{f_{h+1}})\). Let \(a\) be the slope and \(b\) be the y-intercept of the linear line connecting \((t_{f_{h}},px_{f_{h}})\) and \((t_{f_{h+1}},px_{f_{h+1}})\). Then, \(a\) and \(b\), respectively, are computed as follows:  
\[a=\frac{px_{f_{h+1}}-px_{f_{h}}}{t_{f_{h+1}}-t_{f_{h}}},\ \ \ b=px_{f_{h}}-a\times t_{f_{h}}.\]  
All health data that fall into the time interval between \(t_{f_{h}}\) and \(t_{f_{h+1}}\) are represented with a linear line with a slope \(a\) and the y-intercept \((0,b)\).  
Let \(RS_{i}=\left((t_{1},rx_{1}^{i}),(t_{2},rx_{2}^{i}),\cdots,(t_{n},rx_{n}^{i})\right)\) be a reconstructed health data sequence of the \(i\)-th user. Let \(RS=\left\{RS_{1},RS_{2},RS_{3},\cdots,RS_{w}\right\}\) be a set of reconstructed health data of \(w\) wearable device users. Then, the data aggregator computes the aggregate statistics \(AS=\left((t_{1},ax_{1}),(t_{2},ax_{2}),\cdots,(t_{n},ax_{n})\right)\). Here, the value of \(ax_{d}\) at timestamp \(t_{d}\) is estimated using the reconstructed data as follows:  
\[ax_{d}=\frac{1}{w}\times\sum_{i=1}^{w}rx_{d}^{i}.\]  
That is, the average value of the \(d\)-th timestamp is obtained by averaging the values of the \(d\)-th timestamp for all the reconstructed health data.
## 3 Experimental Results and Discussion
### Young's Modulus
#### 3.3.2. Secant Young's Modulus  
The secant Young's modulus \(E_{si}\) for a certain stress-strain curve and the mean and standard deviation values of the secant Young's modulus, \(E_{s}(\varepsilon)\) and \(STD_{E_{s}}.(\varepsilon)\), are obtained from the following equations, the results of which are plotted in Figure 4.  
\[E_{si}(\varepsilon)=\frac{\sigma_{i}}{\varepsilon_{i}} \tag{1}\]  
\[E_{s}(\varepsilon)=\frac{1}{N}\sum_{j=1}^{N}E_{s_{j}}(\varepsilon) \tag{2}\]  
\[STD_{E_{s}}.(\varepsilon)=\sqrt{\frac{1}{N-1}\sum_{j=1}^{N}\left[E_{s_{j}}( \varepsilon)-\overline{E_{s}}(\varepsilon)\right]^{2}} \tag{3}\]  
where \(E_{si}\), \(\sigma_{i}\), and \(\varepsilon_{i}\) denote the \(i\)-th secant Young's modulus, stress and strain for a certain stress-strain curve, respectively. \(E_{s}(\varepsilon)\) and \(STD_{E_{s}}.(\varepsilon)\) denote the values of the mean and standard deviation of the secant modulus for the 12 stress-strain curves, respectively. \(N\) denotes the total numbers of the stress-strain curves.  
Figure 4: _Cont._In Figure 4a, it is observed that the maximum secant modulus ranged from 2350.86 MPa to 4239.79 MPa and underwent different descending branches until to the minimum value ranged from 190.33 MPa to 309.92 MPa at a corresponding strain of 0.0255. In Figure 4b, the mean secant modulus was initially 3359.53 MPa, then decreased with increasing of strain to 233.83 MPa. The \(STD_{E_{s}}(\varepsilon)\) for the specimens is varied from 611.00 MPa to 30.25 MPa. Precisely, it is found that after a dramatic drop before the strain reached 0.0065, the curve of \(STD_{E_{s}}(\varepsilon)\) began to decrease at a slower rate.
## 3 Results
### Cardiac Comorbidities  
Cardiac comorbidities were frequent, with aortopathies (manifest or at risk) being the most common, followed by arterial hypertension, arrhythmias, and pulmonary hypertension/Eisenmenger syndrome. Arterial hypertension was statistically significantly (\(p<0.001\)) associated with overweight/obesity (Table 4).  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Cardiac** & **Total** & \begin{tabular}{l} **Normal** \\ **Weight [BMI]** \\ **18.5–24.9** \\ \end{tabular} & \
\begin{tabular}{l} **Overweight** \\ **[BMI 25–30]** \\ \end{tabular} &
\begin{tabular}{l} **Obesity** \\ **[BMI 30+]** \\ \end{tabular} & \(p\)**-Value** \\ \hline Aortopathy (manifest or at risk) & 754 & 412 (54.6\%) & 253 (33.6\%) & 89 (11.8\%) & 0.348 \\ Art. Hypertension & 129 & 46 (35.7\%) & 48 (37.2\%) & 35 (27.1\%) & 0.001 * \\ Cardiac arrhythmias & 139 & 73 (52.5\%) & 41 (29.5\%) & 25 (18.0\%) & 0.171 \\ Infective endocarditis & 20 & 11 (55.0\%) & 6 (30.0\%) & 3 (15.0\%) & 0.878 \\ Coronary artery disease & 3 & 1 (33.3\%) & 2 (66.6\%) & - & 0.993 \\ Pulmonary hypertension/Eisenmenger & 24 & 7 (29.2\%) & 10 (41.6\%) & 7 (29.2\%) & 0.25 \\ Cyanosis & 23 & 16 (69.6\%) & 4 (17.4\%) & 3 (13.0\%) & 0.207 \\ Other & 56 & 33 (58.9\%) & 14 (25.0\%) & 9 (16.1\%) & 0.157 \\ \hline \hline \end{tabular}  
* Statistically significant (\(p<0.05\)). n = number of patients. BMI = Body Mass Index.  
\end{table}
Table 4: Cardiac comorbidities.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Treatment Status** & \begin{tabular}{l} **Total** \\ **n** \\ \end{tabular} & \begin{tabular}{l} **Normal** \\ **Weight [BMI]** \\ **18.5–24.9** \\ \end{tabular} & \
\begin{tabular}{l} **Overweight** \\ **[BMI 25–30]** \\ \end{tabular} &
\begin{tabular}{l} **Obesity** \\ **[BMI 30+]** \\ \end{tabular} & \(p\)**-Value** \\ \hline Palliative & 17 & 7 (41.2\%) & 8 (47.1\%) & 2 (11.7\%) & 0.678 \\ Surgery & 988 & 540 (54.6\%) & 316 (32.0\%) & 132 (13.4\%) & 0.351 \\ Interventional & 269 & 135 (50.1\%) & 98 (36.4\%) & 36 (13.5\%) & 0.926 \\ Texture & 145 & 83 (57.2\%) & 44 (30.3\%) & 18 (12.5\%) & 0.165 \\ \hline Total & 1419 & 765 (53.9\%) & 466 (32.8\%) & 188 (13.3\%) & - \\ \hline \hline \end{tabular}  
* n = number of patients. BMI = Body Mass Index.  
\end{table}
Table 2: Surgical treatment status.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Systemic** & **Total** & \begin{tabular}{l} **Normal** \\ **Weight [BMI]** \\ **18.5–24.9** \\ \end{tabular} & \
\begin{tabular}{l} **Overweight** \\ **[BMI 25–30]** \\ \end{tabular} &
\begin{tabular}{l} **Obesity** \\ **[BMI 30+]** \\ \end{tabular} & \(p\)**-Value** \\ \hline Right-systemic & 140 & 63 (45.0\%) & 60 (42.9\%) & 17 (12.1\%) & 0.781 \\ Left-systemic & 1133 & 607 (53.6\%) & 367 (32.4\%) & 159 (14.0\%) & 0.404 \\ Univentricular & 146 & 95 (65.1\%) & 39 (26.7\%) & 12 (8.2\%) & 0.001 * \\ Total & 1419 & 765 (53.9\%) & 466 (32.8\%) & 188 (13.3\%) & - \\ \hline \hline \end{tabular}  
* Statistically significant (\(p<0.05\)). n = number of patients. BMI = Body Mass Index.  
\end{table}
Table 3: Systemic ventricle morphology.
## 4 Conclusions  
In conclusion, the dissociative ionization of CF\({}_{2}\)Br\({}_{2}\) in 800 and 400 nm femtosecond laser fields has been investigated by time-of-flight mass spectra and dc-sliced velocity mapping imaging technique. Because the C-F bond is much stronger than that C-Br bond, the main channel of reaction originates from the C-Br bond cleavage. With the aid of the quantum chemical calculations, the reaction pathways of dissociative ionization of CF\({}_{2}\)Br\({}_{2}\) have been assigned. A unique elimination channel has been observed under 400 nm laser fields, and it indicates that reaction pathway is sensitive to laser wavelength. The distinct difference of the angular distribution for ion Br\({}^{+}\) and CF\({}_{2}\)Br\({}^{+}\) suggests that the coupling between the excited states play crucial role in the dissociation dynamics.  
**Supplementary Materials:** The following are available online at [[https://www.mdpi.com/2076-341](https://www.mdpi.com/2076-341)]([https://www.mdpi.com/2076-341](https://www.mdpi.com/2076-341)) 7/11/4/1704/s1: Figure S1: The yield ratio of [Ar\({}^{2+}\)]/[Ar\({}^{+}\)] of our measurement comparing with the same ratio in literatures Ref [1]. The red solid square is the experimental results title, Figure S2: The time-of-flight mass spectra under different laser power, in the (a) 800 nm laser field; (b) 400 nm laser field, respectively, Figure S3: (a) the optimized structure of singly charged ion CF\({}_{2}\)Br\({}^{+}\) at the B3 LYP/6-31G\({}^{*}\) level and the related C-F and C-Br bonds strength calculated by the relax force constant (b) the optimized structure of singly charged ion CF\({}_{2}\)Br\({}^{+}\) at the B3 LYP/6-31G\({}^{*}\) level and the related C-F and C-Br bonds strength calculated by the relax force constant [2]. Table S1: The compliance matrix of singly charged ion CF\({}_{2}\)Br\({}_{2}\)\({}^{+}\) and CF\({}_{2}\)Br\({}^{+}\), which is calculated by B3 LYP level of theory using 6-31G\({}^{*}\) basis set.  
**Author Contributions:** [PERSON] and [PERSON] finished the experiment works and the draft. The theoretical calculations are supervised by [PERSON] [PERSON] proposed the original experiment idea and designed the experiments, and he revised the manuscript and submitted the paper. Z.S. is the funds holder which support our experiments, and participated the experiment design. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work has been partially supported by National Natural Science Foundation of China (No. 12034008 and 11727810), the Science and Technology Commission of Shanghai Municipality (No. 19)C1412200) and the Program of Introducing Talents of Discipline to Universities 111 project (B12024).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the first author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## Appendix B HyspLIT Analysis of Sand Episode Originating from Aralkum Desert
In the week before, for most hours of day, SO2 concentration was approximately 5 \(\upmu\)g/m\({}^{3}\), while in the first 10 days of the emergency state, the median is a bit higher between 7 and 8 \(\upmu\)g/m\({}^{3}\) from midnight to morning hours and about 10 \(\upmu\)g/m\({}^{3}\) later on during the working hours and evening. * Daily profiles for O3, the main pollutant, follow diurnal cycle with maximal levels from 10 to 16 h with a median of about 70 \(\upmu\)g/m\({}^{3}\) in the week before and 80 \(\upmu\)g/m\({}^{3}\) in the first 10 days of the emergency state (Figure A5b). During morning and evening hours, median values are also 10 \(\upmu\)g/m\({}^{3}\) lower in the week before (about 50 \(\upmu\)g/m\({}^{3}\)) than in the first 10 days of the emergency state (60 about \(\upmu\)g/m\({}^{3}\)). Interquartile ranges are larger during the emergency state in periods of day with maximal levels of O3. * Daily profiles for NO, NO2 and NOx at Liman NN2 site, although lower in general, show a clear change in daily patterns similar to Rumenacka NN1 site. NOx boxplots(Figure 14(c)) show clear morning and afternoon pollution peaks, both before and during the emergency state, but the median of these peaks is reduced during the emergency state (median around 30-35 \(\upmu\)g/m\({}^{3}\) before emergency state from 18 to 19 h, and an evident reduction of the median of about 15 \(\upmu\)g/m\({}^{3}\) during the emergency state). In addition, during the most active hours of the day (period from 9 to 17 h), a significant drop in the level of air pollution can be observed, indicated by a lower median (median around 15-30 \(\upmu\)g/m\({}^{3}\) before the emergency state during this time of day, and about 10-15 \(\upmu\)g/m\({}^{3}\) during emergency state during this time of day). * Observing CO daily profiles at Liman (Figure 14(d)), NN2 site, the following conclusions can be reached. During the active hours of the day, from 9 h to 17 h level of CO stayed similar, going between 0.4 and 0.5 mg/m\({}^{3}\), with a narrower interquartile range during the emergency state. During morning hours, the median level is similar, but with larger interquartile ranges before the emergency state. Similar as at the Rumenacka site, from 18 to 23 h, there is a slight reduction in CO level during the emergency state. Daily profiles before and during the emergency state for CO levels are connected with domestic heating sources that stay the same and traffic, leading to a slight reduction.
## 2 Principles of the Proposed Method
### OSNR Monitoring Based on the IBPSO-Based DNN Trained with AHs
#### 2.2.2 Principle of the IBPSO
In this way, the particle swarm can easily approach the global optimal particle eventually and, when the velocity is 0, the probability of a change rate of the particle bit is increased near 0. This idea is in agreement with the essence of particle swarm optimization. With improvements to BPSO, IBPSO finds the best binary vector where each bit is associated with a feature. If a bit of the vector is 1, the feature is selected. If the bit is 0, the feature cannot be selected. We hope to divide the whole feature and eliminate the irrelevant features by their importance. This helps us to reduce the computational overhead, dimension features of datasets, and improve the estimation accuracy. The flow chart of the IBPSO algorithm and the process of optimizing DNN with joint IBPSO and PSO are shown as Figures 5 and 6. As can be seen from Figure 6, the \"initial network\" is the conventional DNN for comparison and the fully connected DNN based on IBPSO has a simpler structure due to the reduced dimension of bin counts of AHs, which reduces the hardware complexity of the sampling module. Figure 5: The flow chart of improved binary particle swarm optimization (IBPSO) algorithm. Figure 6: Process of deep neural network (DNN) optimized by IBPSO.
## 7 The Iterative Framework  
A power trace (PT) or electromagnetic trace (EMT) acquired during an ECSM operation can be regarded as a sequence of its individual samples. The algorithm's iterative nature, i.e., the processing of the key bit by bit, and its consistent time execution provide the opportunity for the attacker to partition the samples into smaller segments, representing the individual iterations of the ECSM operation, i.e., the handling of a single key bit. In the hardware implementation, the ECSM iterations are equally long (54 clock cycles) and processed one after another without any breaks; therefore, there is no need for a trace alignment step, like in software implementations. We refer to segmented portions, the single ECSM iterations, as \"slots\". Within each cycle, there are \(S\) samples, determined by the oscilloscope's sampling rate _Sampling_Rate_ and the clock signal frequency \(F\): \(S=\frac{Sampling\_Rate}{F}\). In our experiments, \(S=625\) samples, applying _Sampling_Rate_ = 2.5 Gsample/sec and \(F=4\) MHz.  
Figure 4: Three cases of model performance: (**a**) underfitting, (**b**) a good bias–variance trade-off, and (**c**) overfitting, given training set samples (red \(X\)).  
Our framework consists of the following:  
* A preprocessing step, wherein we compress all values in each clock cycle, i.e., we represent \(S\) samples in each clock cycle using only a single value, which is calculated corresponding to the selected compression method; for example, it can be the arithmetic mean of all \(S\) samples or the maximum value.
* Then, a horizontal attack is used to derive initial key candidates, which are expected to be somewhat incorrect. We have applied the _comparison to the mean_ method used in [50] to have a baseline for our attack, but it could be some other statistical or machine-learning-based attack, which produces initial key candidates.
* At the end, an iterative classification algorithm is used to improve the labeling and hopefully extract the secret scalar. In [17], we used Support Vector Machines; here, we introduce Artificial Neural Networks to the framework. The classifiers are trained based on the initial key candidates derived from the horizontal attack, i.e., one classifier per key candidate, and new candidates are computed. The new candidates are used for subsequent training until the candidates do not change anymore between iterations. We initialize new ANNs after every iteration of the framework although, according to our experiments, there is no difference in the result of the framework whether we reuse already-trained ANNs from the previous iteration or initialize new ones each time. Both the SVM and ANN methods produce single classification for each slot. We used compressed values as inputs, i.e., there are only 54 features, one for each clock cycle of the ECSM iteration. The general idea is shown in Figure 5.
# Lightweight Integrity Preserving Scheme for Secure Data Exchange in Cloud-Based IoT Systems
## 3 Problem Statement  
### The Traditional Model  
In the integrity checking model, there are three functions--the semi trust server, the smart device user, and the IoT-cloud system. The latter provides services to hold the significant volume of data files owned by the smart device user. The role of the semi-trust server is to validate the integrity of the IoT-cloud system's data, as the smart device user does not have this capability.  
In the traditional schemes shown in Figure 1, the smart device user generates the public and private parameters, computes the metadata of their files using the public parameters, and signs the metadata through employing private parameters. The smart device user also transmits file and metadata to the IoT-cloud system and metadata to the semi-trust server. The smart device user sends a request to the semi-trust server to confirm that the data have been stored in the IoT-cloud system. The semi-trust server generates a challenge request and sends it to the semi-trust server.  
The IoT-cloud system receives the challenge request and begins calculating the verification response through employing the parameters of the challenge request for the metadata that is stored in the cloud. The IoT-cloud system then sends the verification response to the semi-trust server. The semi-trust server receives the verification response and confirms the validity of the response by using a bilinear map. Finally, the semi-trust server sends a  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **lightweight** & **IoT-Cloud System** & **Public Integrity Check** & **Batch Integrity Checking** & **Key Management** \\ \hline Our & Yes & Yes & Yes & Yes & Yes \\ Proposed & No & No & Yes & No & No \\
[7] & No & No & Yes & Yes & No \\
[10] & No & No & No & No & No \\
[11] & No & No & No & No & No \\
[18] & No & No & No & No & No \\
[20] & No & No & No & No & No \\
[22] & No & No  
report to the smart device user regarding the data that are stored in the cloud. The total computation cost of the bilinear map scheme includes numerous modular exponential operations that occur when one file of data is processed. This computation is extremely costly, particularly as substantial information is being audited. However, due to the large number of data tags, their integrity checking protocols will incur a heavy storage overhead on the server. Furthermore, the public-integrity-checking-scheme-based bilinear map incurs a heavy computation cost to the auditor, which makes the integrity checking system inefficient [21].
# A Novel Routing Scheme for Creating Opportunistic Context-Virtual Networks in IoT Scenarios +
## 2 Related Work
In addition, due to infrequent connectivity caused by the lack of network infrastructure and random mobility models followed by devices, the routing can be increasingly complex [9]. To address this problem, the authors of [5] presented MobCCN, a protocol for accessing data generated by IoT devices in the presence of Mobile opportunistic networks. Since it is compliant with the information-centric networking principles, the idea is to populate the forwarding interest base tables of the nodes to guide the propagation of interest packets towards the destinations (i.e., the nodes that store the required data). Therefore, resource inefficient schemes are not used to propagate interest packets as in the case of traditional opportunistic routing schemes, such as epidemic routing [24]. Other research has focused on the application of machine learning technologies for improving routing solutions. For instance, in [9], a new routing protocol, called Gaussian Mixture Model Routing (GMMR), was proposed by applying Gaussian Mixture Models (GMM) and machine learning-based soft clustering mechanisms. GMMR is a method focused on flooding messages to a cluster of similar devices identified by GMM clustering. The features used to train the GMM clustering model help to identify devices with a good message forwarding character. Nevertheless, these approaches require a training phase for clustering the different nodes/devices to which the messages should be forwarded. In [25; 26], the authors implemented a fuzzy-based system for IoT device selection in opportunistic IoT networks. This algorithm uses different parameters, such as message timeout ratio, contact duration, device storage, device waiting time, and device remaining energy, in order to select the best devices to which the messages should be forwarded. This protocol allows the authors to fairly select the devices depending on their capabilities; however, it does not take into account the interests of the devices in storing and managing data. Recently, other routing algorithms for Delay-Tolerant Networks (DTNs), such as [27], focused on an effective energy-oriented path selection and message scheduling defined for reducing the resource consumption. With the goal of reducing the overhead, Geographic-Based Spray-and-Relay (GSaR) [28] and Trajectory-Driven Opportunistic Routing (TDOR) [29] protocols are based on the historical geographic information and the trajectory of nodes for making routing decisions depending on the probabilities of encounters. Thus, the number of copies is reduced, and the delivery ratio is increased. Nevertheless, these approaches do not consider the nodes' interests in order to not overload those nodes that are not related to the transmitted information. This perspective could be interesting for IoT networks in which nodes have limited resources. Having introduced a review of related works on opportunistic IoT networking, the next section describes the situational context concept as a previous step to the definition of OCVNs in Section 4.
# Adaptive Jamming Mitigation for Clustered Energy-Efficient LoRa-BLE Hybrid Wireless Sensor Networks
###### Abstract  
Wireless sensor networks (WSNs) are fundamental for modern IoT applications, yet they remain highly vulnerable to jamming attacks, which significantly degrade communication reliability and energy efficiency. This paper proposes a novel adaptive cluster-based jamming mitigation algorithm designed for heterogeneous WSNs that integrate LoRa and Bluetooth Low Energy (BLE) technologies. The proposed strategy dynamically switches between communication protocols, optimizes energy consumption, and reduces retransmissions under interference conditions by leveraging real-time network topology adjustments and adaptive transmission power control. Through extensive experimental validation, we demonstrate that our mitigation mechanism reduces energy consumption by up to 38% and lowers packet retransmission rates by 47% compared to single-protocol networks under jamming conditions. Additionally, our results indicate that the hybrid LoRa-BLE approach outperforms standalone LoRa and BLE configurations in terms of network resilience, adaptability, and sustained data transmission under attack scenarios. This work advances the state-of-the-art by introducing a multi-protocol interference-resilient communication strategy, paving the way for more robust, energy-efficient, and secure WSN deployments in smart cities, industrial IoT, and critical infrastructure monitoring.  
jamming mitigation; heterogeneous wireless sensor networks; energy-efficient communication; adaptive protocol switching 202525, 25, 1931