esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploiting the Rolling Shutter Read-Out Time for ENF-Based Camera Identification
###### Abstract  
The electric network frequency (ENF) is a signal that varies over time and represents the frequency of the energy supplied by a mains power system. It continually varies around a nominal value of 50/60 Hz as a result of fluctuations over time in the supply and demand of power and has been employed for various forensic applications. Based on these ENF fluctuations, the intensity of illumination of a light source powered by the electrical grid similarly fluctuates. Videos recorded under such light sources may capture the ENF and hence can be analyzed to extract the ENF. Cameras using the rolling shutter sampling mechanism acquire each row of a video frame sequentially at a time, referred to as the read-out time (\(T_{ro}\)) which is a camera-specific parameter. This parameter can be exploited for camera forensic applications. In this paper, we present an approach that exploits the ENF and the \(T_{ro}\) to identify the source camera of an ENF-containing video of unknown source. The suggested approach considers a practical scenario where a video obtained from the public, including social media, is investigated by law enforcement to ascertain if it originated from a suspect's camera. Our experimental results demonstrate the effectiveness of our approach.  
electric network frequency (ENF); rolling shutter mechanism; read-out time, \(T_{ro}\); camera forensics; camera sensor types; ENF extraction +
Footnote †: journal: Applied  
Article  
Exploiting the Rolling Shutter Read-Out Time for ENF-Based Camera Identification
# Releasing Differentially Private Trajectories with Optimized Data Utility
## 5 Experimental Evaluations
### Utility Metrics
#### 5.3.2 Geographic Utility Metrics  
_(i) Negative Hausdorff distance_ is widely used to measure the similarity of two sets of points. It is the negative of the greatest of all the distances from points in one set to the closest point in the other set. Equation (13) presents the definition of _negative Hausdorff distance_ between the actual dataset \(D_{A}\) and obfuscated \(D_{O}\):  
\[Q(f)=-max\{h(D_{A},D_{O}),h(D_{O},D_{A})\} \tag{13}\]  
where \(h(D_{A},D_{O})=max_{l\in D_{A}}\{min_{l^{\prime}\in D_{O}}\{dist(l,l^{\prime} )\}\}\), \(dist(\cdot,\cdot)\) is a distance measurement method, e.g., Euclidean distance. A larger \(Q(f)\) indicates a higher utility in terms of geography.  
_(ii) Negative query error_ refers to the negative value of the query error, where a query is to retrieve the number of trajectories passing through a certain region \(R\). Let \(q\) denote a query and \(q(D)\) its answer on a trajectory database. The query error is defined as the ratio of the difference of query result on \(D_{A}\) and that on \(D_{O}\) to the query result on \(D_{A}\). We generate 1000 random queries \(\mathcal{Q}\) by randomly choosing the region from the map and taking the negative average of all query errors, as shown in Equation (14). A larger \(Q(f)\) implies a higher utility in terms of geography.  
\[Q(f)=-\frac{1}{|\mathcal{Q}|}\sum_{q\in\mathcal{Q}}\frac{|q(D_{A})-q(D_{O})|}{ q(D_{A})} \tag{14}\]  
Note that the reason for using negative values of the query error and the Hausdorff distance is that they can vary with the semantic utility in the same direction.
# Synchronizing TSN Devices via 802.1 AS over 5G Networks
## 5 Simulation Results and Discussion  
### Simulation Scenario 1  
The TSN Switch directly synchronizes to the master via the Ethernet link. The time difference between its clock and the master's clock, after synchronization, is in the order of 100 nanoseconds, thus indicating a normal successful time synchronization. However, the results of the TSN device 2 synchronization show a lower accuracy. Figure 3 shows the clock time differences of the master clock (red line) and TSN device 2's clock (blue line) to the true time. (True time is the actual simulation time, and all the nodes in simulation, including TSN Master, have a constant drift.) It can be seen that the difference between the TSN Master graph and TSN device 2 graphs is approx. 2.5 milliseconds, which means that after synchronization, TSN device 2 still has a 2.5 ms difference from the master's clock time. This low accuracy is explained by Figure 4. Figure 4a shows the uplink propagation time, downlink propagation delay, and their difference. The 802.1 AS standard assumes a symmetric link in terms of propagation delay and computes the average propagation delay, which is used for synchronization. But with a 5G network, the uplink and downlink propagation delay is asymmetric, which is mainly because of the differences in the medium access control (MAC) for uplink and downlink traffic, resulting in an error in propagation delay computation, which in turn affects the accuracy of synchronization.  
Figure 4b shows the box plot of the uplink and downlink delay as well as the actual propagation delay calculated by the 802.1 AS model. From Figure 4, it can be seen that the maximum difference between uplink and downlink propagation delay is approx. ~2 ms. If this difference is considered in the propagation delay calculation, the time difference between TSN device 2 and the Master, after synchronization, reduces to ~0.4 ms at maximum (see Figure 3--TSN device 2 clock time (adjusted)), which is still quite high. The variability of the time difference can be explained by the variability of the uplink and the downlink propagation delay, as seen in Figure 4.  
Figure 3: Time differences to the true time after synchronization.  
The reason for propagation delay differences in the uplink and downlink is the process of how 5G UE obtains access to the channel for uplink transmissions. The UE can only transmit in the uplink when the channel grants are given to the UE, resulting in longer delays which in turn results in asymmetric uplink and downlink propagation delay. Furthermore, QoS support is also absent in the Simu5G framework, which further reduces the accuracy during synchronization since sync messages cannot be tagged as high-priority messages.
## Appendix B Gaussian Process Regression  
A Gaussian process is defined as  
\[g(\mathbf{q})\sim\mathcal{GP}\bigl{(}m(\mathbf{q}),k(\mathbf{q},\mathbf{q}^{ \prime})\bigr{)}, \tag{14}\]  
where \(m(\mathbf{q})=\mathbb{E}(g(\mathbf{q}))\) is the mean function and \(k(\mathbf{q},\mathbf{q}^{\prime})=\mathbb{E}((g(\mathbf{q})-m(\mathbf{q}))(g( \mathbf{q}^{\prime})-m(\mathbf{q}^{\prime})))\) the covariance function of the process. Let us assume that we have a set of noisy observations \(\{\mathbf{q}_{k},\theta_{k}\}_{k=1}^{m}\), \(\theta_{k}=g(\mathbf{q}_{k})+\epsilon\), \(\epsilon\sim\mathcal{N}(0,\sigma_{n}^{2})\), where \(\mathcal{N}\) denotes the Gaussian distribution. In our experiments, \(\theta_{k}\) is one of the parameters describing the motion (one of the CDMP parameters), \(\mathbf{q}\) is the desired target of the throw, and \(g\) is the unknown nonlinear function mapping query points to the parameters of the throwing motion. Subtracting the mean from the training data, we can assume that \(m(\mathbf{q})=0\). If we are given a set of \(m_{2}\) new query points \(\mathbf{Q}^{*}=\{\mathbf{q}_{k}^{*}\}_{k=1}^{m_{2}}\), then the joint distribution of all outputs is given as [39]  
\[\left[\begin{array}{c}\mathbf{\theta}\\ \mathbf{\phi}^{*}\end{array}\right]\sim\mathcal{N}\biggl{(}\mathbf{0},\left[\begin{array} []{cc}\mathbf{K}(\mathbf{Q},\mathbf{Q})+\sigma_{n}^{2}\mathbf{I}&\mathbf{K}( \mathbf{Q},\mathbf{Q}^{*})\\ \mathbf{K}(\mathbf{Q}^{*},\mathbf{Q})&\mathbf{K}(\mathbf{Q}^{*},\mathbf{Q}^{*} )\end{array}\right]\biggr{)}, \tag{15}\]  
where \(\mathbf{Q}=\{\mathbf{q}_{k}\}_{k=1}^{m}\), \(\mathbf{Q}^{*}\), \(\mathbf{\theta}=[\theta_{1},\dots,\theta_{m}]^{\text{T}}\), \(\mathbf{\theta}^{*}\), respectively, combine all inputs and outputs and \(\mathbf{K}(\cdot,\cdot)\) are the joint covariance matrices calculated according to the model (14). Based on joint distribution (15), the expected value \(\tilde{\mathbf{\theta}}^{*}\in\mathbb{R}^{m_{2}}\) can be calculated as [39]  
\[\tilde{\mathbf{\theta}}^{*}=\mathbb{E}(\mathbf{\theta}^{*}|\mathbf{Q},\mathbf{\theta}, \mathbf{Q}^{*})=\mathbf{K}(\mathbf{Q}^{*},\mathbf{Q})[\mathbf{K}(\mathbf{Q}, \mathbf{Q})+\sigma_{n}^{2}\mathbf{I}]^{-1}\mathbf{\theta}, \tag{16}\]  
with the following estimate for the covariance of the prediction,  
\[\text{cov}(\mathbf{\theta}^{*})=\ \ \mathbf{K}(\mathbf{Q}^{*},\mathbf{Q}^{*})-\mathbf{K}( \mathbf{Q}^{*},\mathbf{Q})[\mathbf{K}(\mathbf{Q},\mathbf{Q})+\sigma_{n}^{2} \mathbf{I}]^{-1}\mathbf{K}(\mathbf{Q},\mathbf{Q}^{*}).\]One commonly used covariance function is  
\[k(\mathbf{q},\mathbf{q}^{\prime})=\sigma_{f}^{2}\sum_{i=1}^{D_{q}}\exp\left(- \frac{1}{2}\frac{(q_{i}-q_{i}^{\prime})^{2}}{l_{i}^{2}}\right), \tag{10}\]  
which results in a Bayesian regression model with an infinite number of basis functions. \(D_{q}\) denotes the dimension of the query point space. \(\sigma_{n}^{2}\), \(\sigma_{f}^{2}\), and \(l_{i}\) are the hyperparameters of the Gaussian process that need to be estimated in the training phase. See in [39] for more details.
## 4 Experimental Results
### Application
#### 4.7.3 Experiment Process  
To meet the different key lengths of standard AES based on cipher block changing (CBC), our key generation model is trained to generate 128-, 192-, and 256-bit bio-keys from the collected dataset during the training stage. For different sizes of bio-keys, we only make a slight modification on the output dimension in the DNN architecture. Subsequently, our key generation model with trained DNN parameters is deployed into the user computer.  
As is shown in Figure 9, we give the encryption and decryption process. Our test model consists of face detection, key generation model, AES encryption, and decryption modules. In the encryption process, we adopt the USB camera to obtain a face image of the user; then, this face image is detected through the face detection module by MTCNN.  
Next, a bio-key is generated by our trained key generation model. At the same time, the plaintext is submitted as an input of the AES encryption module. Finally, we perform data encryption with the inputs of the bio-key and plaintext to yield a ciphertext. In the decryption process, we also obtain different face images of the same user to regenerate the bio-keys, which are used to decrypt the ciphertext for generating plaintext. Therefore, we compare the data consistency.
## 2 Materials and Methods  
The following materials were used for testing:  
* Quartz sand sourced from the Szczakowa mine (Sibelco Poland Sp. z o. o., Gdansk, Poland);
* A Geopol inorganic binder, along with a dedicated SA72 hardener produced by Sandteam (Holubice, Czech Republic), which was used to prepare the molding sands--the detailed characterization of the binder is available in a previous publication [29; 30];
* The PO1-PO3 perlite ore from the Slovak deposit [31];
* The PO1-PO3 perlite ore from the Hungarian deposit [32].  
Equipment used for testing:  
* An LPzE-2e laboratory shaker with a set of sieves (Multiserw Morek, Marcyporeba, Poland);
* A device to test the strength of the molding sands, LRu-2e (Multiserw Morek);
* A device to test the permeability of the molding sands, LPiR-3e (Multiserw Morek);
* A device to test the grindability of the molding sands (Huta Stalowa Wola, Stalowa Wola, Poland). The principle of this apparatus is as follows: a standardized cylindrical specimen, made of a hardened molding sand, is seated in a holder using a clamp. The clamped specimen is placed in rotary motion at a speed of 1 rpm/s by an electric motor via a gearbox. The electric motor is powered directly from the 220 V mains. At the top of the apparatus, a steel shot container is placed, with a funnel-shaped bottom ending in a hole (\(\O\)7 mm), closed by a slide bolt. The steel shot from the container falls during marking with a tube (from a height of 307 mm) on the rotating shaper and causes its abrasion. The separated molding sand together with the shot falls into the tank. During the measurement, the specimen is enclosed in a shield, which prevents the shot and molding sand from spreading sideways.  
Each time, the molding sand was prepared according to the following recipe:  
* Quartz sand--100 parts by mass;
* Geopol--2.5 parts by mass compared to quartz sand;
* Hardener SA72--8% parts by mass compared to the Geopol binder;
* Loosening additive (perlite ore)--5 parts by mass compared to quartz sand.  
The tensile strength of eight standard shapes (standard sample, 70 mm in length, with neck dimensions of 22.36 mm \(\times\) 22.36 mm) at ambient temperature was determined 24 h after sample preparation. Permeability and grindability tests were conducted on standard cylindrical shapes (\(\O\)50 \(\times\) 50), also 24 h after sample preparation. To determine the final tensile strength (\(R_{m}^{Rk}\)), the samples were subjected to heating in an oven to a specific temperature within the range of 100-1000 \({}^{\circ}\)C. The heating rate was constant at 1 degree per minute, and the samples were held at the set temperature for 15 min. Strength tests were performed on samples cooled to ambient temperature (cooling together with the furnace). All presented results represent the arithmetic mean of three measurements.
## 2 Methods
### Outcome Measures
#### 2.5.2 Questionnaires  
Job Satisfaction Questionnaire (MSQ)Job satisfaction is commonly understood as the array of feelings or affective responses linked to the job environment, or simply put, \"how people feel about different aspects of their jobs\" [60]. In 1967, [PERSON] et al. developed the short version of the Minnesota Satisfaction Questionnaire (MSQ), which employs a 5-point Likert-type scale comprising 20 items. This instrument has shown strong stability over time [61]. A systematic review by [PERSON] [62] reported that the MSQ had an internal consistency of 0.92, a test-retest reliability of 0.89, and a convergent validity of 0.83.  
General Health QuestionnaireThis questionnaire developed by the research team is composed of three questions asking a participant to evaluate on a five-point scale (1 = very bad to 5 = very good) their overall health, and, separately, their physical and then mental health.  
Cognitive Change Questionnaire (CCQ)The CCQ [63] used during the COVID-19 pandemic was modified for the self-assessment of cognitive change in this study. The questionnaire comprised nine items, self-reporting changes observed in everyday cognitive functions, specifically related to the speed of information processing, short-term storage, prospective memory, attention, and executive control. Participants were instructed to compare their cognitive functions before and after CCT, using a seven point scale. For items one to five, the scale ranged from 1 = \"much less often\" to 7 = \"much more often\" compared to before the CCT. For items 6-9, the scale ranged from 1 = \"much easier\" to 7 = \"much harder\" than before the CCT. Scores on the CCQ range from 9 to 35, with higher scores indicating a greater self-perceived decline in cognitive functioning.  
_Satisfaction With Life Scale (SWLS)_: The SWLS [64] is a five item scale designed to measure overall life satisfaction based on an individual's own subjective criteria. This scale has been widely utilized and demonstrates internal consistency and temporal stability estimates ranging from 0.54 for a 4 year interval to as high as 0.89 for a two week interval [65]. Participants rate their agreement with each statement using a seven point scale, ranging from 1 = \"strongly disagree\" to 7 = \"strongly agree\". Scores on the SWLS range from 5 to 35, with higher scores indicating greater life satisfaction.  
_The WHO-5 Well-Being Index:_ As observed in Ref. [66], this is a widely used questionnaire designed to measure well-being [66]. It consists of five items, such as \"Over the last month I have felt cheerful and in good spirits,\" which are rated on a six point Likert scale. Responses range from 0 = \"never\" to 5 = \"all the time\". Higher scores on the WHO-5 indicate a greater state of well-being. The WHO-5 has been validated in numerous studies and has demonstrated good psychometric properties [67].
## 2 Materials and Methods
### Data Source  
The alpine grassland data are sourced from the MOD12Q1 (land cover) dataset provided by MODIS Terra + Aqua, with a temporal resolution of 1 year and a spatial resolution of 500 m. Evapotranspiration (ET) data are derived from the MOD16A2 GF dataset from MODIS, with a temporal resolution of 8 days and a spatial resolution of 500 m. All data are available from MODIS ([[https://ladsweb.modaps.eosdis.nasa.gov/](https://ladsweb.modaps.eosdis.nasa.gov/)]([https://ladsweb.modaps.eosdis.nasa.gov/](https://ladsweb.modaps.eosdis.nasa.gov/))).  
NPP data are sourced from the National Earth System Science Data Center ([[http://www.geodata.cn](http://www.geodata.cn)]([http://www.geodata.cn](http://www.geodata.cn))), part of the National Science and Technology Infrastructure Platform. These data are derived from the GLASS GPP dataset, using the TRENDY model to compare the ratio of plant autotrophic respiration to GPP simulated by 10 dynamic vegetation models, which generates the GLASS NPP product. The temporal resolution of the data is 8 days, and the spatial resolution is 500 m.  
The climate variable data used in this study are sourced from multiple platforms. precipitation and temperature data are obtained from the China Regional Meteorological Element Driving Dataset (CMFD) ([[http://data.tpdc.ac.cn](http://data.tpdc.ac.cn)]([http://data.tpdc.ac.cn](http://data.tpdc.ac.cn))), with a temporal resolution of 1 day and a spatial resolution of 0.1\({}^{\circ}\)[44]. Soil moisture data are sourced from the China Soil Moisture/Soil Water Dataset (2000-2020) ([[http://data.tpdc.ac.cn](http://data.tpdc.ac.cn)]([http://data.tpdc.ac.cn](http://data.tpdc.ac.cn))). This dataset includes high temporal and spatial resolution soil moisture data for the period from 2000 to 2020, with a daily temporal resolution and measurements at 10 cm intervals across 10 soil depth layers (10-100 cm). For this study, which focuses on alpine grasslands, the surface 10 cm soil moisture data were selected, with a temporal resolution of 1 day and a spatial resolution of 0.1\({}^{\circ}\)[45]. Soil temperature data and vapor pressure deficit (VPD) data are both sourced from the ERA5-Land reanalysis dataset ([[https://cds.climate.copernicus.eu](https://cds.climate.copernicus.eu)]([https://cds.climate.copernicus.eu](https://cds.climate.copernicus.eu))). The data have  
Figure 1: Study area.  
a temporal resolution of 1 h and a spatial resolution of 0.1\({}^{\circ}\). The VPD is calculated based on the temperature and dew point temperature. The photosynthetically active radiation (PAR) data are sourced from the Global Long-Term High-Resolution Photosynthetically Active Radiation Dataset ([[http://data.tpdc.ac.cn](http://data.tpdc.ac.cn)]([http://data.tpdc.ac.cn](http://data.tpdc.ac.cn))), with a temporal resolution of 1 day and a spatial resolution of 10 km [46].  
In order to match the spatial resolution of the meteorological data, all datasets in this study were interpolated to a spatial resolution of 0.1\({}^{\circ}\). The study period selected was from 2001 to 2018.
# Speaker-Independent Spectral Enhancement for Bone-Conducted Speech
## 4 Experiment
### Subjective Evaluation  
The subjective evaluation method is different from the above method, which is based on the evaluation of people. In this paper, the mean opinion score (MOS) [41] was used to evaluate the auditory perception of the enhanced BC speech. A total of 20 subjects (10 male and 10 female, with good subjective auditory perception function) participated in the subjective test. During the evaluation, the BC speech before and after the enhancement is played at random, and then the subjects subjectively score the spoken words. The score range of MOS in this evaluation is 0-5 points. The higher the score, the better the subjective auditory quality of enhanced BC speech. The MOS evaluation criteria are shown in Table 7 below.  
The calculation method of the MOS score is shown as follows,  
\[Score_{MOS}=\frac{1}{M\times N}\sum_{i=1}^{M}\sum_{j=1}^{N}\text{ score }_{i,j} \tag{15}\]  
where M represents the number of people participating in the evaluation, and N represents the number of speech pieces evaluated by each person. \(\text{score}_{i,j}\) is the \(ith\) evaluation.  
In this experiment, we tested the \(Score_{MOS}\) of five BC enhancement methods, respectively. The \(Score_{MOS}\) of all models is intuitively shown in Figure 12. The score of our method is better than other methods. It can be seen from the results in the figure that the enhanced BC speech enhanced by our proposed BC enhancement method has higher clarity and naturalness.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Score** & **Quality Level** & **Subjective Perception** \\ \hline
0–1 & Bad & Very uncomfortable and intolerable \\
1–2 & Poor & Uncomfortable but tolerable \\
2–3 & Fair & Perceived distortion and uncomfortable \\
3–4 & Good & Slight distortion but not annoying \\
4–5 & Excellent & Imperceptible \\ \hline \hline \end{tabular}
\end{table}
Table 7: Sound quality scoring standard.  
Figure 11: STOI scores of enhanced BC speech obtained by different methods.
# A Hybrid Data-Fusion System by Integrating CFD and PNN for Structural Damage Identification
## 3 Simulation Validation
### Comparison and Discussion
The eigen-level model is specialized in the case of measured data with enormous samples and uncertainties, and for the case of confidence level of each sensor is determined ahead, the decision-level model is the best choice; the single PNN model is considered only when the data collected is somehow limited, or few sensors have been installed.