esa-sceva/satcom-chunk-collection huggingface dataset:
# HSM4 SSL: Leveraging HSMs for Enhanced Intra-Domain Security +
## 3 HSM4 SSL Architecture
### HSM4 SSL: Low-Level Architecture
#### 3.3.3 Hardware Abstraction Layer  
The HAL, shown in Figure 6, is the heart of the HSM4 SSL. It enables software applications to communicate with different HSMs by abstracting the low-level details of the underlying HSMs. It contains a _service manager_ and a _driver manager_. The service manager acts as an intermediary between the controller managers and the underlying hardware, while the driver manager handles the execution of hardware-specific commands.  
The HAL communicates with the flow manager to obtain the policy set. The policy set provides instructions on various aspects, such as security measures, access control, error handling, and resource utilization, based on analyzing the security requirements, capabilities, and current utilization of each HSM. Additionally, the policy set may include guidelines for access control, specifying which entities or users have permission to perform certain operations on the HSMs.  
When a request is received from the controller managers, the service manager translates the request into a feature request. This translation process entails applying the policy set to ensure the inclusion of the appropriate destination HSM parameter in the message. By adhering to these policies, the service manager guarantees that the feature request contains the necessary information to route it to the correct HSM. The driver manager maintains a registry that maps each feature request to the appropriate HSM's command and driver. This registry serves as a lookup table, allowing the driver manager to identify the specific hardware command and corresponding driver that can handle the requested feature.
## 4 Experimental Results
### Generalization to Unseen Spoof Materials  
To further assess the model's ability to handle unseen spoof types, several representative out-of-distribution spoof materials were tested individually. The AUC and EER results are presented in Figure 14.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Spoof Score** & **EER** & **AUC** & \(\textbf{BPCER}_{\textbf{10}}\) \\ \hline \(\sigma\)Score & 0.0751 & 0.9568 & 0.0882 \\ \hline \(\mu\)Score & 0.0965 & 0.9329 & 0.1128 \\ \hline SpoofScore\({}_{\mu-\sigma}\) & **0.0668** & **0.9748** & **0.0562** \\ \hline SpoofScore\({}_{G}\) & 0.0852 & 0.9637 & 0.0711 \\ \hline \end{tabular}
\end{table}
Table 6: Ablation study on anomaly score determination.  
\begin{table}
\begin{tabular}{l c c} \hline
**Model** & **FLOPs (G)** & **Parameters (M)** \\ \hline AE & 2.47 & 3.14 \\ VAE & 1.64 & 53.5 \\ MAE & 1.59 & 3.14 \\ OCPAD & 1.59 & 3.14 \\ PPAD & 6.15 & 10.87 \\ UOCFR & 3.87 & 8.65 \\ Ours & 5.62 & 9.31 \\ \hline \end{tabular}
\end{table}
Table 7: Comparison of model complexity and computation cost.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Comparison** & **Metric** & \
\begin{tabular}{c} **Ours** \\ **(Mean \(\pm\) STD)** \\ \end{tabular} &
\begin{tabular}{c} **Baseline** \\ **(Mean \(\pm\) STD)** \\ \end{tabular} & \(\textbf{\emph{p}-Value}\) \\ \hline Ours vs. PPAD & AUC & 0.9731 \(\pm\) 0.0071 & 0.9553 \(\pm\) 0.0034 & \(<\)0.0001 \\  & EER & 0.0654 \(\pm\) 0.0102 & 0.0749 \(\pm\) 0.0112 & 0.0759 \\  & BPCER\({}_{10}\) & 0.0571 \(\pm\) 0.0096 & 0.0673 \(\pm\) 0.0101 & 0.0420 \\ \hline Ours vs. U-OCFR & AUC & 0.9731 \(\pm\) 0.0071 & 0.9665 \(\pm\) 0.0066 & 0.0495 \\  & EER & 0.0654 \(\pm\) 0.0102 & 0.0790 \(\pm\) 0.0119 & 0.0180 \\  & BPCER\({}_{10}\) & 0.0571 \(\pm\) 0.0096 & 0.0702 \(\pm\) 0.0148 & 0.0401 \\ \hline \end{tabular}
\end{table}
Table 8: Paired \(t\)-test results comparing our method with PPAD and U-OCFR.
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 4 Evolution of Communications Technologies for Telemedicine
### Mobile Networks
#### 4.5.5 Small Cells  
Small cells are located within specific clusters, covering User Equipment (UEs) and sharing of resources with the base station. Small cells are located within close proximity to users, providing continuous connection to the user and supporting macrocells that have wider coverage. Small cell usage encourages better frequency reuse and management. Provision of small cells is a high point in 5G wireless technology at the mm-wave frequency. The various types of small cells and their coverage radii are shown in Table 5.  
Telemedicine has benefited from the offerings of 5G, especially in terms of high-speed medical information sharing in emergency cases, where physicians can use high-quality video for prompt and accurate decision-making. 5G networks provide massive M2M, D2D, IoT, and IoWT services with high reliability. Moreover, 5G can be used for more advanced applications such as Augmented Reality and Virtual reality [40] for eHealth systems [118]. Nonetheless, there are a number of challenges involved in the use of 5G for telemedicine, as listed in [40]:  
1. Scalability of devices in cases where there are many connected devices, meaning that device gateways and protocols need to be carefully designed;
2. IoT connectivity;
3. Resource optimisation;
4. Interoperability;
5. Energy requirements of connected devices;
6. Data privacy, security, and trust;
7. Enormous data computation.
## 2 Experimental Details
### Testing Program  
Usually, one of two methods is used to determine the shear strength of unsaturated soil. One method is to use a direct shear instrument or triaxial equipment, which employs axial translation technology to control suction [20; 38]. Although this method has high accuracy, it is difficult to widely apply in engineering practice owing to its slow shear rate, long testing period, and high operating cost. Instead, the combined methods of suction measurement and a conventional direct shear test are widely accepted in practice [39; 40]. The related research literature has demonstrated that the results obtained by the two methods are near-identical [41]. That is, the shear strength of unsaturated soil determined by conventional direct shear tests already implies the contribution of matric suction. Based on this, the latter method is introduced to survey the shear behavior of undisturbed loess and paleosol samples under various initial moisture contents.  
A fully automatic direct shear apparatus was employed to perform direct shear tests with a maximum shear displacement of 10 mm. Its shear rate in the process of testing was set to 0.2 mm/min, which can be considered a drainage test [42]. The normal stresses of 50 kPa, 100 kPa, 200 kPa, and 400 kPa were imposed during the direct shear test.  
The SWCC was measured by the pressure membrane instrument method in this work. For each soil sample (i.e., loess and paleosol), sections of 20 kPa, 40 kPa, 60 kPa, 80 kPa, 100 kPa, 150 kPa, 200 kPa, 250 kPa, 300 kPa, 390 kPa, and 480 kPa were applied in sequence, respectively. After each level of suction balance, the mass of the soil sample was weighed until the final suction level was reached.
# Hardware-Accelerated Data Readout Platform Using Heterogeneous Computing for DNA Data Storage
## 5 Modules of Hardware Acceleration Kernel
### Primer Sequence Identification Module  
The primer sequence identification module precisely identifies the forward primer sequences at the beginning of each read based on Hamming distance calculations. The primer sequence identification module consists of multiple Hamming distance calculation units, a multi-level comparison network, a reverse complement calculation unit, and a splitting unit (Figure 5). When the 256-bit sequencing reads are output from the FIFO and enter the module, the splitting unit extracts the 40-bit forward primer sequence and the 144-bit index and payload sequence.  
The Hamming distance calculation unit consists of a logic gate array and a parallel adder tree, enabling the parallel computation of Hamming dist  
Figure 5: Primer sequence identification module.  
forward primers and 12 reference primers within a single clock cycle. The multi-level comparison network, implemented with 23 comparators, identifies the minimum value among 24 Hamming distances through a step-by-step comparison process and outputs the corresponding reference primer ID. A comparator checks whether the minimum Hamming distance meets a preset threshold and outputs a 1-bit validity signal. The reverse complement unit control signal, derived from a modulo-2 operation on the 4-bit primer ID, indicates whether the sequencing read corresponds to the reverse strand. It determines whether the original sequence or the reverse complement of the index and payload sequences is output through the dual path selector.  
The proposed primer sequence identification architecture utilizes multiple parallel combinational logic units. These units operate in a pipeline manner, delivering high processing throughput while maintaining identification accuracy.
## 4 DLR+ Router Overview  
In this section, we describe in more detail the fundamental principle and architecture of DLR+, the router in the proposed solution. The main idea is to have a router capable of learning from the conditions of its environment and use such information to make smart forwarding decisions. In order to achieve that, the router uses two pre-trained feed forward neural networks to process the information from both its neighbors and the messages in their queues in real time and select from them the best next hop for the best next message, according to their current fitness. More details are given in the following subsections.
# Numerical Study on the Application of Near-Infrared Temperature Distribution Measurement of HIFU
## 1 Introduction
These studies have expanded the methods of HIFU temperature monitoring and demonstrated the potential application of near-infrared temperature measurement technology in the field of HIFU. In basic HIFU experiments, in vitro tissues are typically immersed in water. However, with water adhering to the surface of the tissue, infrared cameras may struggle to obtain the actual surface temperature accurately. Utilizing infrared glass for auxiliary temperature measurement is a possible solution to address this issue. Infrared glass has good transmittance and a low refractive index in the near-infrared band, while being very sensitive to heat. It can provide accurate temperature measurements in high-temperature environments and maintain stability and reliability. Infrared glass is positioned near the tissue surface to circumvent direct contact with moisture, thereby eliminating the temperature errors caused by such contact. This method not only overcomes the limitations of thermocouples in traditional methods but also records the temperature distribution and diffusion, offering new insights for using near-infrared temperature measurement methods. This study primarily investigates the impact of infrared glass on the temperature distribution of high-intensity focused ultrasound in near-infrared temperature measurement. Due to inherent differences in the characteristic parameters between infrared glass and the surrounding tissues, viscous heating is inevitably introduced. Therefore, the increase in the temperature of tissues near-infrared glass was calculated through numerical simulations. Moreover, a detailed analysis of the nonlinearity, shear waves, and thermal viscosity was also conducted. In addition, particular focus was put on the effects of oblique incidence on shear waves and viscous heating. The results of this study will provide an essential reference for the application of near-infrared technology in HIFU temperature measurement practice.
###### Abstract  
A healthcare information system allows patients and other users to remotely login to medical services to access health data through the Internet. To protect the privacy of patients and security over the public network, secure communication is required. Therefore, the security of data in transmission has been attracting increasing attention. In recent years, blockchain technology has also attracted more attention. Relevant research has been published at a high rate. Most methods of satisfying relevant security-related regulations use modular and exponential calculation. This study proposes a medical care information preservation mechanism that considers the entire process of data storage in devices from wearable devices to mobile devices to medical center servers. The entire process is protected and complies with HIPAA privacy and security regulations. The proposed scheme uses extended chaotic map technology to develop ID-based key negotiation for wearable devices, and thereby reduces the amount of computing that must be carried out by wearable devices and achieve lightness quantify. It also uses the non-tamperability of the blockchain to ensure that the data have not been tampered with, improving data security. The proposed mechanism can resist a variety of attacks and is computationally lighter than the elliptic curve point multiplication that has been used elsewhere, while retaining its security characteristics.  
blockchain; chaotic maps; HIPAA; access control; healthcare information; information security; patient privacy +
Footnote †: journal: Medical Informatics and Institute of Medical Sciences, Tzu Chi University,  
Hualen 97004, Taiwan; [EMAIL_ADDRESS] (T.-F.L.); [EMAIL_ADDRESS] (T.-S.K.)1
# Review on Semi-Fragile Watermarking Algorithms for Content Authentication of Digital Images
## 4 Evaluation Indexes  
For semi-fragile watermarking algorithms applied to authenticate the image content, the indicators, which are used to evaluate their performance, should be able to reflect the invisibility of the watermark, the accuracy of tamper detection, and the ability of recovery. The invisibility of the watermark means that the host image should not undergo significant degeneration after embedding the watermark. The accuracy of tamper detection, which can be reflected by the false positive rate (FPR) and the false negative rate (FNR), is directly related to the quality of the recovered image. The higher the detection accuracy, the better the quality of the image may be recovered.
## 2 Materials and Methods
### Machine Learning Test Method
#### 2.2.4 Model Accuracy Evaluation Metrics  
Undoubtedly, accuracy is the most significant criterion in the evaluation of a statistical model. Therefore, five commonly used error metrics were used for the model evaluation and comparison, including RE, R\({}^{2}\), MABE, MAPE, and RMSE. The equations and descriptions of the five metrics used in our study are presented in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Equation** & **Description** \\ \hline \multirow{2}{*}{MABE} & \(\frac{1}{\pi}\sum_{i=1}^{n}\left|f_{i}-y_{i}\right|\) & \multirow{2}{*}{\begin{tabular}{c} MABE is the absolute value of the bias error that is as low as possible. MABE provides knowledge about the long-term performance of prediction models [58; 59]. \\ \end{tabular} } \\  & \(\frac{1}{\pi}\sum_{i=1}^{n}\left|\frac{f_{i}-y_{i}}{y_{i}}\right|\times 100\) & \multirow{2}{*}{\begin{tabular}{c} As with MABE, MAPE is calculated in the form of a percentage. The smaller the MAPE value, the better the model performance [60; 61]. \\ \end{tabular} } \\  & \(\frac{f_{i}-y_{i}}{y_{i}}\times 100\%\) & \multirow{2}{*}{\
\begin{tabular}{c} The relative error (RE) is the ratio of the absolute error of a measurement to the measurement being taken [23]. \\ \end{tabular} } \\  & \(\sqrt{\frac{1}{\pi}\sum_{i=1}^{n}\left(y_{i}-f_{i}\right)^{2}}\) & \multirow{2}{*}{
\begin{tabular}{c} RMSE is the square root of the ratio of the square of the deviation between the predicted value and the true value to the number of observations. A smaller RMSE value always represents a better performance [60]. \\ \end{tabular} } \\  & \(1-\frac{\sum\left(f_{i}-y_{i}\right)^{2}}{\sum\left(f_{i}-y_{i}\right)^{2}}\) & \\ \end{tabular}
\end{table}
Table 3: Descriptions of error metrics used to evaluate the prediction accuracy of machine learning models.  
In Table 3, \(f_{i}\) and \(y_{i}\) are the predicted populations and the census populations of streets, respectively; \(\overline{y}_{i}\) is the mean of the census population; \(n\) is the quantity of subdistricts.
## 3 Details of the Proposed Algorithm
### Residues, Stopping Criteria, and Penalty Parameter Tuning  
The convergence of the ADMM algorithm is typically assessed through primal and dual feasibility conditions. Following a similar derivation process of the termination criteria for standard ADMM [25], the primal residue \(\mathbf{r}_{p}\) and dual residue \(\mathbf{r}_{d}\) for our algorithm are given by  
\[\mathbf{r}_{p}^{(k+1)}=\mathbf{\Phi}\mathbf{x}^{(k+1)}+\mathbf{z}^{(k+1)}- \mathbf{y},\quad\mathbf{r}_{d}^{(k+1)}=\rho\mathbf{\Phi}^{H}(\mathbf{r}_{p}^{ (k+1)}-\mathbf{r}_{p}^{(k)})-\frac{1}{t_{x}^{(k+1)}}(\mathbf{x}^{(k+1)}- \mathbf{x}^{(k)}). \tag{34}\]  
The iteration stops when the following conditions are met:  
\[||\mathbf{r}_{p}^{(k)}||_{2}\leq\epsilon_{p}^{(k)}\quad\text{and}\quad|| \mathbf{r}_{d}^{(k)}||_{2}\leq\epsilon_{d}^{(k)} \tag{35}\]  
where \(\epsilon_{p}^{(k)}\) and \(\epsilon_{d}^{(k)}\) are updated using absolute and relative criteria:  
\[\epsilon_{p}^{(k)}=\sqrt{M}\epsilon_{\text{abs}}+\epsilon_{\text{rel}}\max\{|| \mathbf{\Phi}\mathbf{x}^{(k)}||_{2},||\mathbf{z}^{(k)}||_{2},||\mathbf{y}||_{ 2}\},\quad\epsilon_{d}^{(k)}=\sqrt{N}\epsilon_{\text{abs}}+\epsilon_{\text{ rel}}||\mathbf{\Phi}^{H}\gamma^{(k)}||_{2}. \tag{36}\]  
Here, \(\epsilon_{\text{abs}}\) and \(\epsilon_{\text{rel}}\) denote the absolute and relative tolerances, respectively. In addition, to balance the convergence of primal and dual residues and to reduce the sensitivity of the performance to the initial value of penalty parameter \(\rho\), a widely applied adjustment strategy is employed:\[\rho^{(k+1)}=\left\{\begin{array}{ll}\delta^{\rm incr}\rho^{(k)}&\text{if }|| \mathbf{r}_{p}^{(k)}||_{2}>\xi||\mathbf{r}_{d}^{(k)}||_{2}\\ \delta^{\rm decr}\rho^{(k)}&\text{if }||\mathbf{r}_{p}^{(k)}||_{2}<\xi^{-1}|| \mathbf{r}_{d}^{(k)}||_{2}\\ \rho^{(k)}&\text{otherwise.}\end{array}\right. \tag{37}\]  
Commonly used values for the constants in (37) are \(\xi=10\) and \(\delta^{\rm incr}=\delta^{\rm decr}=2\).  
Incorporating the stopping criteria and the penalty parameter adjustment scheme outlined above, the complete pseudocode for the proposed algorithm is summarized in Algorithm 4.  
```
1:Initialization: Set \(\rho\), \(\tau\), \(\varepsilon\), \(\epsilon_{\rm abs}\), \(\epsilon_{\rm rel}>0\).
2:for\(k=1,2,3,\dots\)do
3: Update the primary variable \(\mathbf{x}^{(k)}\) with the non-monotone strategy in Algorithm 2.
4: Update the auxiliary variable \(\mathbf{z}^{(k)}\) by Algorithm 3.
5: Update the dual variable \(\boldsymbol{\gamma}^{(k)}\) by (15c).
6: Compute the primal and due residues by (34) and the tolerances by (36).
7:if stopping criteria in (35) are met then
8:break
9:endif
10: Update the penalty parameter \(\rho^{(k)}\) by (37).
11:endfor
```  
**Algorithm 4** Proposed Algorithm
## 8 Hidden Modes and Model Simplification  
Hidden modes and compartments also have an upside in the context of model simplification. For state variable models, model **reduction (aggregation)** means finding a simpler representation (with fewer state variables) appropriate or useful for some purpose. Transfer functions (TFs) \(H_{ij}\)(s) between pairs of inputs and outputs (\(u_{j}\) and \(y_{i}\)) of multicompartmental models \(\dot{\mathbf{q}}=K\mathbf{q}+B\mathbf{u},\mathbf{y}=C\mathbf{q}\) are particularly convenient for this because _pole-zero cancellations_ in \(H_{ij}\)(s) reduce model dimensionality and therefore the state variable and compartment number between these I-O pairs. This means that a multicompartmental  
Figure 5: Mode determination by visual inspection for a more complex 3-compartment model.  
model has hidden compartments if its TF has at least one pole-zero cancellation. Conversely, it has no hidden compartments if there are no pole-zero cancellations [21]. (We remark here that multicompartmental models also can have hidden oscillations (**hidden oscillatory modes**) in some compartments with complex eigenvalues (modes) hidden from outputs measured in a central compartment. This interesting phenomenon can occur in potentially physiologically realizable systems, modeled with _generalized mammillary models_, as developed in [21].  
**Example 4: 3- Compartment Candidate Model Transfer Function and Simplifications**  
The I-O TF for the model of Example 3, Figure 5, is computed by using Laplace transform analysis as:  
\[H_{31}(s)=\frac{k_{21}k_{32}}{s(s^{2}+(k_{21}+k_{12}+k_{02}+k_{32})s+k_{21}(k_ {02}+k_{32}))} \tag{3}\]  
No pole-zero cancellation is possible in (3) with \(k_{\bar{q}}>0\), so model dimensionality cannot be reduced from 3. If both the input and output are instead in compartment 2 here (TF \(H_{22}\)), the TF is:  
\[H_{22}(s)=\frac{s(s+(k_{12}+k_{02}+k_{32}))}{s(s^{2}+(k_{21}+k_{12}+k_{02}+k_{3 2})s+k_{21}(k_{02}+k_{32}))} \tag{4}\]  
The zero and pole at the origin in (4) cancel each other, and the model is reduced to the second order. And if turnover rates in compartments 1 and 2 (\(-k_{11}=k_{21}\) and \(-k_{22}=k_{12}+k_{02}+k_{32}\)) are equal, we still have a two-dimensional model:  
\[H_{22}(s)\big{|}_{k_{21}=k_{12}+k_{02}+k_{32}}=\frac{s+k_{21}}{\left(s+k_{21} \right)^{2}-k_{21}k_{12}} \tag{5}\]  
Although not the intention of this example, the number (two) of visible compartments in this \(H_{22}\) example is available simply from the inspection of the graph: the mode generated in compartment 3 cannot be returned to and therefore be visible in 2 because there is no path from 3 to 2.
# Earth Rotation Parameters Estimation Using GPS and SLR Measurements to Multiple LEO Satellites
## 6 Conclusions
With more satellites of new GNSS [47; 48] being equipped with laser retroreflectors dedicated to SLR today, the study can be extended by including SLR observations to GNSS satellites, to realize the combination of GNSS, LEO, and LAGEOS for the realization of a reference frame in the future. Conceptualization, X.L. and H.Z. ; methodology, X.L. and H.Z. ; software, X.L. ; validation, H.Z. and K.Z. ; formal analysis, H.Z. ; investigation, H.Z. and K.Z. ; resources, Y.Y. and Y.Q. ; data curation, H.Z. ; writing--original draft preparation, H.Z. ; writing--review and editing, K.Z. and H.Z. ; visualization, H.Z. ; supervision, W.Z. ; project administration, X.L. ; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (grant 41774030, grant 41974027 and grant 41974029), the Hubei Province Natural Science Foundation of China (grant 2020 CFA002), the frontier project of basic application from Wuhan Science and Technology Bureau (grant 2019010701011395), and the Sino-German mobility programme (grant No. M-0054). We are grateful to the International GNSS Service (IGS) for providing the precise orbit, clock and correction products of GPS satellites at [[ftp://cddis.gsfc.nasa.gov](ftp://cddis.gsfc.nasa.gov)]([ftp://cddis.gsfc.nasa.gov](ftp://cddis.gsfc.nasa.gov)) (accessed on 1 June 2021) for free. The ILRS is acknowledged for providing SLR observations of LEO and LAGEOS satellites. The data onboard GRACE-FO, Swarm, and Sentinel-3 are publicly available from [[ftp://isdcftp.gfz-potsdam.de](ftp://isdcftp.gfz-potsdam.de)]([ftp://isdcftp.gfz-potsdam.de](ftp://isdcftp.gfz-potsdam.de)) (accessed on 1 June 2021), [[ftp://swarm-disse.ee.esa.int](ftp://swarm-disse.ee.esa.int)]([ftp://swarm-disse.ee.esa.int](ftp://swarm-disse.ee.esa.int)) (accessed on 1 June 2021) and [[https://sichub.copernicus.eu/gnss](https://sichub.copernicus.eu/gnss)]([https://sichub.copernicus.eu/gnss](https://sichub.copernicus.eu/gnss)) (accessed on 1 June 2021) respectively. The numerical calculations in this study have been done on the supercomputing system in the Supercomputing Center of Wuhan University. The authors declare no conflict of interest.