esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Hardware-Software Integration in Cryptographic Accelerators for Industrial IoT
## 2 Preliminaries and Related Work
### Related Work on HW Support to Security  
In the current state of the art, in order to accelerate the computing of the main security functions, many hardware (HW) secure co-processors have been proposed. The proposed approaches at the state of art typically follow one of these two approaches: off-chip Trusted Platform Model (TPM) and on-chip Hardware Security Module (HSM). In the TPM approach, standardized as ISO/IEC 11889, a dedicated security chip implements in HW some security functions that are offloaded from the main application processor. The TPM secure chip is assembled in the same electronic board with the application processor chip and they communicate through a serial data interface such as I2C or SPI. TPM chips available in the market typically support secure keys storage and authentication and encryption functions. For example, the ST33 GTPMAI2C chip [18] is compliant with Trusted Computing Group (TCG) TPM Library spec. 2.0 and includes: AIS-31 Class PTG2 compliant true random number generator (TRNG): FIPS compliant DRBG (Deterministic Random Bit Generator); SHA-1, SHA-2 (256 and 384 bits) and SHA-3 (256 and 384 bits) hashing; AES-128, 192 and 256 bits and Triple DES (Data Encryption Standard) 192 bits symmetric encryption; RSA key generation (1024, 2048 bits), signature and encryption; ECC (NIST P-256/384) Key generation, ECDH (Elliptic-curve Diffie-Hellman) encryption, ECDSA (Elliptic-curve Digital Signature Algorithm) signature/verification. The limitation of this approach is that the communication between the application processor and the secure chip usually represents a performance bottleneck. The second approach foresees to modify the architecture of the application processor with an on-chip peripheral that acts as a secure instruction set extension. Examples of this approach are the crypto-cell IPs from ARM. For example, the crypto-cell312 [19] is conceived to be integrated on-chip with Cortex-M or Cortex-R 32b processors in IoT applications. The crypto-cell312 achieves 200 MHz in 40 nm technology and supports in hardware HASH functions, such as SHA1, SHA2-256, HMAC (Hash-based message authentication code), Symmetric cryptography Engine for AES (Advanced Encryption Standard) with 128-bit keys and Chacha20, Public-key cryptography based on RSA and Elliptic Curve Discrete Logarithm problem. Being co-integrated on-chip with the application core the security core does not suffer from bandwidth bottlenecks as in case of off-chip TPM chips. However, the secure designer should carefully develop the driver and low-level software (SW) needed to access from the application SW the secure services provided by the HSM.
# Hierarchical Fusion of Machine Learning Algorithms in Indoor Positioning and Localization
## 2 Materials and Methods
### Feature Generation  
For the method employed in the study, no data based on the user id, timestamp, phone id, and space id were used. The study is based on the production of a new dataset in order to provide an alternative to a system that is less applicable due to the dependence of the data on a specific location, user, phone, or WAP number. Also, the generated features are explained in Table 2. In this table, abbreviations of generated features are presented according to the data type and each of these features are explained. Moreover, the absolute Pearson cross-correlation coefficients of the generated data are shown in Figure 3. According to this figure, building, longitude, and latitude values are highly correlated. Also, FirstMAC, SecondMAC, and ThirdMAC features provide better correlation for output values.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **Type** & **Explanation** \\
**Abbreviation** & & \\ \hline FirstMAC & Categorical & WAP ID of the highest RSSI value. \\ SecondMAC & Categorical & WAP ID of the second-highest RSSI value. If not, use the First WAP ID. \\ ThirdMAC & Categorical & WAP ID of the third-highest RSSI value. If not, use the Second WAP ID. \\ LastMAC & Categorical & WAP ID of the lowest RSSI value. If not, use the Third WAP ID. \\ FirstP & Numeric & RSSI value of the first WAP. \\ SecondP & Numeric & RSSI value of the second WAP. If not, use the First WAP RSSI. \\ ThirdP & Numeric & RSSI value of the third WAP. If not, use the Second WAP RSSI. \\ LastP & Numeric & RSSI value of the last WAP. If not, use the Third WAP RSSI. \\ NSSID & Numeric & Number of WAP with a RSSI level between 0 and \(-\)104. Value of +100 is excluded because it means there is no signal according to the dataset. \\ MeanP & Numeric & Mean RSSI values of WAP with a RSSI level between 0 and \(-\)104 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Generated features.  
Figure 3: Absolute cross-correlation coefficients of dataset.
# Analysis of Information Availability for Seismic and Volcanic Monitoring Systems: A Review
###### Abstract  
Organizations responsible for seismic and volcanic monitoring worldwide mainly gather information from instrumental networks composed of specialized sensors, data-loggers, and transmission equipment. This information must be available in seismological data centers to improve early warning diffusion. Furthermore, this information is necessary for research purposes to improve the understanding of the phenomena. However, the acquisition data systems could have some information gaps due to unstable connections with instrumental networks and repeater nodes or exceeded waiting times in data acquisition processes. In this work, we performed a systematic review around information availability issues and solutions in data acquisition systems, instrumental networks, and their interplay with transmission media for seismic and volcanic monitoring. Based on the SLR methodology proposed by [PERSON], a search string strategy was considered where 1938 articles were found until December 2021. Subsequently, through selection processes, 282 articles were obtained and 51 relevant articles were extracted using filters based on the content of articles mainly referring to seismic-volcanic data acquisition, data formats, monitoring networks, and early warnings. As a result, we identified two independent partial solutions that could complement each other. One focused on extracting information in the acquisition systems corresponding to continuous data generated by the monitoring points through the development of mechanisms for identifying sequential files. The other solution focused on the detection and assessment of the alternative transmission media capabilities available in the seismic-volcanic monitoring network. Moreover, we point out the advantage of a unified solution by identifying data files/plots corresponding to information gaps. These could be recovered through alternate/backup transmission channels to the monitoring points to improve the availability of the information that contributes to real-time access to information from seismic-volcanic monitoring networks, which speeds up data recovery processes.  
information security; availability; seismic-volcanic monitoring; seismic-volcanic networks; seismological data centers; algorithms +
Footnote †: journal: Sensors
# Digital Identity in the EU: Promoting eIDAS Solutions Based on Biometrics
## 4 Biometric Identification as a Unique Opportunity
### Empirical Validation  
To validate the proposed method, it is essential to have a dataset comprising both 3D and 2D facial data of the subjects. Specifically, this dataset should include a controlled image of the subject (gallery), a 3D model of the face from which a clean, frontal snapshot can be extracted (probe for our method), as well as a selfie taken under uncontrolled conditions (probe for standard method). By comparing the gallery with the two different probes, using any facial recognition algorithm, it becomes possible to assess which input yields superior recognition results. From a literature review, two facial datasets have been identified that contain both required samples: the FRGC dataset from NIST [43] and the Bosphorus database provided by Philips Research and Bogazici University [44]. In our experiments, we used RetinaFace [45] to detect and crop the face and ArcFace [46] as the machine learning face recognition algorithm. The face images were then individually processed through the model to obtain face vectors, which could be compared using the cosine distance. A lower cosine distance indicates greater similarity between the two feature vectors, suggesting a higher likelihood that the two pictures are of the same face. This distance can be thresholded to form a binary classification decision of match or no match and calculate True Positive and False Negative statistics.  
As shown in Table 3, which presents the results of the ROC curve analyses, the gain in using 3D data to build the probe images, as opposed to using uncontrolled data, is substantial. The table presents the values of the True Positive Rates (TPR) at two different False Acceptance Rates (FARs), demonstrating that, for the FRGC dataset, even under restrictive conditions (FAR = \(10^{-6}\)), our method retains a good level of accuracy (76%), while the standard falls to values not acceptable for an authentication system (11%). The advantage of using the 3D data with Bosphurus is not as evident as in the case of FRGC since the FAR = \(10^{-6}\); both methods reach nearly the same accuracy of 88%. The main reason for this is that the ambient illumination in the Bosphorus acquisitions is uniform across all expressions and poses, and the setting is constant (no background in any of the images). This means that the so-called \"uncontrolled\" images are, in fact, highly controlled in terms of illumination and background. In contrast, the uncontrolled images from the FRGC dataset exhibit more realistic characteristics. For a more extensive analysis of the validation of the proposed method, please refer to the paper [32], which provides detailed descriptions and results of a large number of experiments.
# Design Procedure of a Mono-Anchor Head for 2360 MPa High-Strength Steel Strand
## 2 Performance Criterion of Mono-Anchor Head  
PTI [11] contains performance standards with a stable margin for safety that are meant to reduce the damage to the anchor head due to the tension introduced into the strand. A static load test of the anchoring system is recommended, including the anchorage devices and the strands, with unloading after loading 95% of the minimum tensile strength (MUTS) of the strands, followed by an evaluation of any permanent deflection of the anchor head after disassembly of the anchoring components. The permanent deflection is measured at the top of the anchor head, as shown in Figure 1a, and the corresponding allowance criteria should not exceed 1/600 th of the clear span. However, it is difficult to evaluate the performance of a mono-anchor head by applying the aforementioned method. Instead, the residual deformation around the wedge hole can be taken as a parameter of performance for a mono-anchor head because the permanent deflection of the anchor head is closely related to deformation of wedge holes.  
In this study, a new performance evaluation method for a mono-anchor head is proposed by replacing the vertical deflection with the lateral deformation of the wedge hole. The deflection of the anchor head top surface suggested by PTI [11] can be assumed to be the relative depth into which the wedge slips in Figure 1(b3). Assuming that the material of the wedge and the strand is rigid, due to the slip depth of the wedge, the diameter of the wedge hole (\(D_{i}\)) becomes wider. Accordingly, the deformation of the wedge hole diameter in a mono-anchor head can be converted to the above-mentioned relative depth of the wedge by Equation (1).  
\[\Delta D_{P}=2\times\Delta H_{W}\times\tan(\theta_{W}) \tag{1}\]  
The allowable criterion for the relative depth of the wedge (\(H_{W}\)) in the anchor head to be analyzed is 0.0373 mm, which is 1/600 th of the clear span length of 22.38 mm of the used anchor head in this study. Therefore, the increase in the allowable diameter of wedge hole (\(\Delta D_{W}\)), which will be the performance criterion of the mono-anchor head, is calculated by Equation (1) as 0.0009 mm. When recalculating the plastic hoop strain at the top of the wedge hole (\(\epsilon_{P,Hoop}=\Delta D_{P}/D_{i}\)), a performance parameter, the maximum acceptable \(\epsilon_{P,Hoop}\) is 0.000283.
# Detection of Rock Mass Defects Using Seismic Tomography
## 2 Methodology  
### Theoretical Arrival Time Calculation  
The first step was to accurately compute the arrival times in a complex, anisotropic, and strongly heterogeneous medium. The velocity and anisotropic structures were defined along a regular orthonormal grid with relatively coarse spacing, typically larger than the wavelength of the elastic waves used to probe the system. We chose the fast-marching algorithm, which allowed us to compute arrival times at all nodes in a given grid from a source. The implementation used second-order finite differences to solve the anisotropic Eikonal equation. To mitigate inaccuracies regarding the source point, the trial arrival times at all points in a cubic box surrounding the source point were computed using analytical solutions, assuming a constant velocity and an anisotropy parameter equal to the source [21]. The wavefront for an active source (Figure 1, left) and a passive acoustic emission (Figure 1, right) on a 100 mm \(\times\) 100 mm anisotropic 2D plane were calculated using the above method.
# Fusion of Linear and Mel Frequency Cepstral Coefficients for Automatic Classification of Reptiles
## 4 Experimental Procedure  
This section describes the datasets and the experimental methodology used in the experiments to evaluate the effectiveness of the proposed method.  
To ensure independence between the training and testing sets in each experiment (at least 100 simulations by experiment), the syllables obtained automatically from the segmentation of each sound have been randomly shuffled and split 50/50 into two datasets--one for training and another for testing (\(k\)-fold cross-validation with \(k=2\))--to achieve significant results.  
Furthermore, accuracy has been calculated following Equation (5) for each class and averaging the results. F-Measure value [28] has also been calculated as \(2*((P*R)/(P+R))\), where \(P\) (precision) is the number of correct positive results divided by the number of all positive results, and \(R\) (recall) is the number of correct positive results divided by the number of positive results:  
\[\text{Accuracy}(\%)=\frac{\text{Syllables Correctly Identified }(N_{c})}{\text{Total Number of Syllables }(N_{s})}\times 100. \tag{5}\]  
The acoustic classification system was implemented in Matlab, where the SVM implementation was based on the libsvm library [29], applying a C-Support Vector Classification (C-SVC) [30].  
In addition, a non-dedicated standard laptop with an Intel Core i7-4510 2.0 GHz CPU and 16 GB RAM under Windows 8.1 operating system was used to carry out the experiments.  
As indicated in Section 2.2, the number of cepstral features was obtained by experimentation. They were selected applying a wrapper method by varying the number of coefficients, \(N\), from 6 to 25 for each feature until there was no improvement in prediction.  
As for the SVM parameters, to find the optimum values of the penalty parameter of the error term (C) and the kernel gamma (\(\gamma\)) parameter, a grid-search was utilized with exponentially growing sequences of the parameters (\(C=2^{-2},2^{-1},...,2^{10}\); \(\gamma=2^{-12},2^{-11},...,2^{2}\)) and employing cross-validation. Finally, a finer grid search was conducted, establishing a gamma value of 0.45 and a penalty term of 30 for all experiments.  
Initially, the features have been analyzed individually to determine their effectiveness. However, different species of reptiles can produce sounds in frequencies anywhere in the spectrum. Therefore, to obtain the correct useful information, the two types of cepstral coefficients have been fused to better represent the acoustic information of the sounds.
## 3 Results
### Influence of Different Cropland Use Transitions on GPP
#### 3.2.3 Increase in GPP Due to Cropland Compensation  
Guangdong Province has accumulated a total of 89.7 thousand km\({}^{2}\) of compensated cropland, experiencing significant increases in cropland area across all regions except for EG (Figure 8). Among them, the PRD has seen the largest increase in cropland area, reaching 27.6 thousand km\({}^{2}\). However, the cities with the largest increases in cropland area are Zhanjiang and Maoming in the WG, with 13.3 thousand km\({}^{2}\) and 8.24 thousand km\({}^{2}\), respectively, accounting for 24.07% of the total increase. Examining different periods, the additional cropland area decreased from 33.9 thousand km\({}^{2}\) in 1991-2000 to 25.6 thousand km\({}^{2}\) in 2010-2020. This represents an average annual decrease of 276.83 km\({}^{2}\), aligning with the trend observed in non-agriculturalized areas.  
Unlike the other two types of cropland use transitions, complemented cropland can enhance crop planting area and GPP (Table 5). From 1991 to 2020, Guangdong Province increased its GPP by 40.02 million tons due to additional cropland, with an average annual increase of 1.33 million tons. Among them, the PRD saw the largest increase in GPP, reaching 12.12 million tons. Specifically, Zhanjiang and Maoming, having the largest increases in cropland area, contributed significantly to the increase in GPP, with increments of 8.96 million tons and 4.04 million tons, respectively. Overall, additional compensated cropland has played a crucial role in stabilizing cropland areas and enhancing agricultural production capacity in Guangdong Province.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Districts**} & \multicolumn{3}{c}{**Area (km\({}^{2}\))**} & \multicolumn{3}{c}{**GPP (10\({}^{4}\) t)**} & \multicolumn{3}{c}{**Area (km\({}^{2}\))**} & \multicolumn{1}{c}{**GPP (10\({}^{4}\) t)**} \\ \cline{3-10} \multicolumn{2}{c}{} & **1991–2000** & **2001–2010** & **2011–2020** & **1991–2000** & **2001–2010** & **2011–2020** & **1991–2020** & **1991–2020** \\ \hline \multirow{4}{*}{NG} & Meizhou & 1608.55 & 1050.54 & 1657.29 & 22.98 & 17.81 & 31.86 & \multirow{4}{*}{469.98} \\  & Yunfu & 1327.34 & 1391.09 & 1213.15 & 21.33 & 24.41 & 26.75 & \\ \cline{1-1}  & Shaoguan & 1157.28 & 897.27 & 1415.19 & 29.26 & 21.91 & 44.20 & 22,360.76 & \\ \cline{1-1}  & Qingyuan & 2107.13 & 1698.49 & 2129.01 & 47.30 & 38.81 & 56.26 & \multirow{4}{*}{469.98} \\ \cline{1-1}  & Heyuan & 1943.87 & 1038.97 & 1725.60 & 32.60 & 18.14 & 36.35 & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._  
Figure 8: The impact of cropland compensation on GPP in Guangdong province from 1991 to 2020.
# An Admission Control Mechanism for 5G LWA
## 3 System Model
### LWA Activation and Deactivation Procedures  
When UEs initiate LWA service requests by selecting option 3 (i.e., LWA mode), the eNB requires UEs to perform WLAN measurement in order to receive information on WLAN radio conditions (i.e., a WLAN connection status report). The UEs perform WLAN measurement to detect the status of the connected WLAN AP. The WLAN measurement report is comprised of the received signal strength indicator (RSSI), the WLAN identifier, WLAN carrier information, channel utilization, a station count, the admission capacity, the WLAN rate, the backhaul rate, and information about UEs connected to the WLAN. Based on the WLAN measurement report (a RRC report) on the RRM database, the eNB executes the UEs' selection algorithm residing on a LB entity for LWA activation or deactivation by configuring the bearer at the RRM layer. The detailed algorithm run by eNB is shown in Figure 3. The procedure is divided into six major parts, which are (a) user capability inquiry, (b) eNB configuration of WLAN measurements, (c) measurement configuration/report, (d) eNB informs the WT about user capability, (e) LWA activation, and (f) feedback/flow control.
# Detection of Volatile Organic Compounds (VOCs) in Livestock Houses Based on Electronic Nose
## 2 Materials and Methods  
### Experimental Samples  
Two farms (one pig farm and one chicken farm) were selected as the test site, which located at the City of Panshi, Jilin Province, China. To study the distribution and change law of the gas in the house in time and space, the sampling locations selected were the exhaust outlet and the middle position in the pregnant pig house and the middle position and window in the nursery pig house. The sampling point was 1 m away from the ground, which was close to the breathing area of the pig; in this way, it could protect the sampling device from damage. Manure was cleaned at 6:00, feeding was being performed at 13:00, and the house was quiet at 18:00. So, gas collections were performed at 6:00, 13:00, and 18:00, which were recorded as morning, afternoon, and evening, respectively. The chicken house was selected as the sampling location in the middle and window of the house.  
The sample collection times were 8:00 and 16:00, which were recorded as morning and afternoon, respectively. The manure removal was completed during the gas collection in the morning, and the food was provided during the afternoon gas collection.  
The QC-1S air sampler (Beijing Institute of Labor Protection Science) was used for gas collection, and the sampling flow rate was set to 1.5 L/min. Four sampling points in the two pig houses were sampled for three consecutive days, and a total 36 gas samples of 30 L were collected. At the same time, 2 L gas samples were collected at 6:00 and 13:00 in the middle position of the nursery pig house and in the window position of the nursery pig house, respectively. Two sampling points in the chicken house were sampled for two consecutive days, and a total 8 gas samples of 30L were collected. At the same time, 2 L gas samples were collected at 6:00 and 14:00 in the middle position of the chicken house and 14:00 in the window position of the chicken house, respectively. The gases that were collected from different sampling positions and times were named nursery-middle-morning, nursery-middle-afternoon, nursery-middle-evening, nursery-window-morning, nursery-window-evening, pregnancy-middle-morning, pregnancy-middle-afternoon, pregnancy-middle-evening, pregnancy-exhaust-morning, pregnancy-exhaust-afternoon, pregnancy-exhaust-evening, chicken-middle-morning, chicken-middle-afternoon, chicken-window-morning and chicken-window-afternoon, respectively. The 30 L sample was used for the electronic nose test, and the 2 L sample was used for gas chromatography. After the gas was collected, the samples were taken back immediately to the laboratory in the dark for testing.