esa-sceva/satcom-chunk-collection huggingface dataset:
# Clinical Brain Monitoring with Time Domain NIRS: A Review and Future Perspectives
## 5 Current Developments in TD-NIRS and Clinical Pertinence
### Instrumental Progress  
In recent years, a great deal of progress has been made in TD-NIRS instrumentation. One of the issues that has to be addressed is to reduce the footprint of the instruments, often considered as bulky. Indeed, even though current systems are useable in a clinical context, the current generation are often mounted on racks which are significantly larger than typical CW-NIRS systems [57]. Reducing the footprint of instruments is therefore crucial in a clinical context where space is limited. Figure 6 illustrates newly developed hardware that illustrates the miniaturisation of TD-NIRS hardware by showing recently developed elements like miniaturized laser sources or detectors [124; 125] no larger than few millimetres. Using these newly developed components, a compact two channel, two wavelength system which fits in a box has been reported, as presented in Figure 6e [126]. This illustrates well the future possibilities of TD-NIRS with instrumental footprints comparable to CW-NIRS systems already used in the clinic. Moreover, these new components have the great advantage of being cheap, which will reduce the overall cost of a TD-NIRS system, facilitating even more its implementation.  
Another line of developments of TD-NIRS systems in order to make the measurement easier to handle for medical staff is non-contact imaging. Few non-contact TD-NIRS system are reported in the literature [127]. Such systems smooth the user experience since no experipositioning. However, such imaging systems would be limited to specific applications, probably to the prefrontal cortex, since hairy regions are particularly difficult to image with such systems. Nevertheless, it is an avenue that needs to be explored. Other systems based on non-fibre emission and detection, in contact with the skin of the patient, have also been reported [128]. These systems not only remove the optical fibres, but also increase the SNR by reducing light losses.
## 5 System Description  
In our system, each participating entity has a unique role, which necessitates corresponding access rights. The following participants are identified:  
* Children: the primary end-users who engage with the application, generating valuable data for monitoring communication development.
* Parents: serving as legal guardians for the children, providing information and medical records, and overseeing access to data by third parties to ensure informed consent.
* Clinicians: external actors responsible for monitoring children's progress. They require access to specific data related to the child's development but not direct access to all personal information due to confidentiality reasons.  
An overview of our system's architecture is depicted in Figure 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Aspect** & & **Description** \\ \hline \multirow{6}{*}{Core Protections} & \(\bullet\) & End-to-end encryption using AES-256 secures data in transit and at rest. \\  & \(\bullet\) & Selective disclosure ensures users reveal only what is necessary. \\  & \(\bullet\) & Unlinkability via fresh tokens and privacy-preserving credentials. \\ \hline \multirow{2}{*}{Offline Attack Resistance} & \(\bullet\) & Even if partial credential data is exposed, offline brute-force attacks are infeasible due to threshold cryptography and zero-knowledge password proofs \\ \hline \multirow{2}{*}{Session and Metadata Privacy} & \(\bullet\) & No party can track users across sessions or learn user identities without consent. \\  & \(\bullet\) & Metadata is anonymized and unlinkable across RPs. \\ \hline \multirow{6}{*}{Limitations} & \(\bullet\) & If a user device is fully compromised (including password/token), impersonation is possible for the token’s lifetime. \\  & \(\bullet\) & Simultaneous compromise of all vIdPs allows unauthorized credential issuance, though this is mitigated through audit logs. \\  & \(\bullet\) & Model manipulation and data poisoning attacks remain a residual risk in Federated Learning. \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._  
Figure 1: General outline of the system.
# 5G NPN Performance Evaluation for I4.0 Environments
## 3 Hybrid 5G and I4.0 Concept Architecture
### Monitoring  
The monitoring component refers to the supervision and observability of the entire data space infrastructure, covering both OT and IT resources. Manufacturing domain monitoring focuses on two aspects of the infrastructure, and aims to improve the data processing of raw machining data. The first aspect addresses the manufacturing process optimization, targeting defect minimization and significant energy reduction, while the second aspect concentrates on predictive maintenance of the machining tools, which can also greatly contribute to OPEX reduction and equipment effectiveness. As optimal production is pivotal in the age of I4.0, Overall Equipment Effectiveness (OEE) has emerged as a Key Performance Indicator (KPI) in the manufacturing industry. OEE benchmarks the percentage of manufacturing time that is truly productive. An OEE score of 100% indicates that one is manufacturing only good parts, as fast as possible, and with no downtime. The OEE KPI formula incorporates three elements: availability (in terms of downtime), performance (the speed of production), and quality (the percentage of products up to standard). Regarding the industry domain, convergence of 5G and I4.0 focuses on extension of the OPC 40501-1 specification targeted for machine monitoring, integrating it across the process chain, where suitable translators can be implemented per demand. Subsequently, for the 5G NPN domain, an I4.0 framework can follow and integrate the directives of the Network Data Analytics Function (NWDAF) 3 GPP specification (33.521). A 5G monitoring solution can be a purpose-built standalone NWDAF, in order to provide a fully standards-based 5G analytics capability. Not merely an API connection to a central analytics platform, but direct integration to an AI-enabled edge platform for intelligent data processing, can provide real-time feedback on the 5G IT and OT states, thus offering practical insights on the factory floor.
# Survey of Localization for Internet of Things Nodes: Approaches, Challenges and Open Issues
## 4 Localization Techniques
### Device-Free Localization
#### 4.2.3 Ultra-Wide Band Radar Localization  
UWB radar is a device-free localization technique comprised of transmitting and receiving nodes, and it is used for detecting and tracking a mobile object in the surveillance region. UWB localization scheme combines the frameworks of operator-based ultra-wide bands, whereas conventional schemes are built on energy detection [68]. Additionally, the proposed scheme permits determining object location in the microenvironment irrespective of the localization error. Localization of an interior mobile object using the transmitter and multiple receivers of the UWB radar is presented [69]. Particle filtering is also used for localization affected by canopy zones by focusing on the projected locations of the particles.
## 1 Introduction
[8] has reported a protocol using an on synchronized LED blinking pattern and keychains that provides a visual confirmation of the sensor pairing. Later, [PERSON] et al. [9] presented another protocol using both public key and secret key cryptography in the authentication. In 2014 [PERSON] et al. [10] improved the anonymity over their previous work and presented a protocol focusing on the communication between the first level and second level nodes using the elliptic curve cryptogrpahy and bilinear map. Moreover, the anonymity of the scheme was broken by [PERSON] in 2014 [11]. [PERSON] and, subsequently, [PERSON] et al. [12] presented their protocols to overcome some weakness founded in previous works. Those protocols however require the use of public key cryptography (either elliptic curve cryptography or bilinear pairing) in the sensor node yielding a heavy computation and storage  
Figure 1: Architecture of a medical WBAN. bundle [13]. In order to save resources and ensure anonymity, [PERSON] et al. [14] proposed a cloud-aided lightweight authentication protocol. Their protocol ensures that the network manager cannot realize the user's real identity in the authentication phase. The sensors attached on the human bodies have direct access to the physiological signals of the person. As a result, following the electrocardiogram (ECG) or photoplethysmogram (PPG), the use of these physiological signals may be used to generate keys of the communication [15; 16; 17]. Such an approach is quite novel and can be possibly developed in good applications after its robustness and security may be verified in a larger scale or experiments. Unlike secrets, and like passwords or pre-loaded secret keys, the physiological signal may not be necessarily kept away from the attackers. In 2017, [PERSON] and his colleagues proposed a lightweight mutual authentication and key agreement protocol with anonymity for the WBAN [4]. They claimed that their protocol provides anonymity and may be secure against various types of attacks. However, this study demonstrates that [PERSON]'s protocol is not secure while the first level node is being compromised. In addition, their approach fails to provide the node anonymity so that an attacker is able to track a second level node. To overcome these shortcomings, we provide a simple but effective amendment for the protocol. The repaired protocol is secured against impersonation attacks, replay attacks, and man-in-the-middle attacks. It also provides better anonymity of the WBAN users. The organization of the paper is as follows. Section 2 reviews the [PERSON]'s scheme. In Section 3, we show the insecurity of their scheme. Next, an improvement scheme will be presented in Section 4. We then provide some security analysis on the improved scheme, and finally conclude the paper.
## 3 Results and Discussion
### Anova Analysis  
A total of 14 responses were evaluated with statistical methods, and the values considered for each run were the average values of the measurements among three repetitions of the same run (Table 8). As previously stated, with the aim to evaluate only artificial lights effects, all the other parameters were kept as constant, thereafter, temperature and humidity were controlled during all the experiments to avoid environmental conditioning. Temperature and humidity were kept almost constant near 19 \({}^{\circ}\)C and 60% respectively, with restrained variation around 5% due to the employment of an indoor environment not perfectly conditioned. The normal distribution of the residuals, as well as their homogeneity, was analyzed (data not reported) for each response, confirming that each mathematical model derived can be used to explore the region of interest. ANOVA results are presented in Table 9 and Figures 5-10. Models correlating the different lights conditions (in single or interaction) to growth and germination performance are significant as confirmed by the \(p\) value < 0.05 for all the responses, indicating that there are any factors that are significant and unknown for the data variation. Moreover, the curvature is not significant, suggesting that the central points can be treated as additional data in the regression model, augmenting the design plan. R\({}^{2}\) and Pred-R\({}^{2}\) (Table 9) confirms the overall good fit of the data, with only two responses with unacceptable quality, _Number of plants_ and _Days for germination_ having R\({}^{2}\) < 0.45. These two responses are mainly related with the germination phase that according to literature is the more critical to model [28]. However, a particular good fitting of the model is shown by _LAI_, _Dry mass_, _Stem dry mass_ and _Water_ responses with values of R\({}^{2}\) equal or above 0.90. The great majority of the resulting models allowed to describe the relationships among light conditions and the measured response. Considering the models' equations (Table 9) not only the _Distance_ plays a valuable role to define the growth performance, as already assessed by the preliminary observation, but also the presence of the _White_ LED and the proportion among hyper red and deep blue must be considered for most of the response. Thereafter, all the independent variables investigated in this study are relevant to define the growth performance of basil. This result is a new finding with respect to a previous research in which LED lights effect on basil germination and growth were investigated in combination to NPK fertilizer obtained in a circular economy perspective [28]. Figure 4: Basil plants at the end of the growth test: (**a**) Sample 6, (**b**) Sample 12, (**c**) Sample 19, (**d**) Sample 2, (**e**) Sample 8, and (**f**) Sample 15. All the sample numbers are referred to in Table 6.
# Advances in Artificial Intelligence for Perception Augmentation and Reasoning
## 3 Medical Image Processing  
Medical images can reflect the internal structure of the human body and have become one of the main foundations of modern medical diagnosis. Using image-processing technology to analyze and process images can greatly improve the efficiency, accuracy, and reliability of clinical diagnosis.  
The key to image navigation surgery is to find the transformation relationship between the spatial position of the preoperative 3D image and intraoperative 2D image, i.e., 2D/3D image registration. In '2D/3D Multimode Medical Image Registration Based on Normalized Cross-Correlation', [PERSON] et al. [5] proposed an image registration algorithm based on normalized cross-correlation. The improved linearity based on the LoG operator increases the edge information of the image as well as the internal details such as isolated points and lines. The improvement based on the Sobel operator increases the sensitivity of rotation transformation, thus increasing the convergence region while improving the accuracy. Given the computational efficiency problems caused by the normalized cross-correlation algorithm, the multiresolution algorithm was adopted. This method has higher registration accuracy, and the multiresolution strategy can reduce the initial value sensitivity of the registration algorithm and reduce the probability of the algorithm falling into the local optimum. In '2D/3D Multimode Medical Image Alignment Based on Spatial Histograms', [PERSON] et al. [6] proposed a feature-based 2D/3D medical image registration algorithm which extracts the statistical features of the image using the gradient direction spatial weighted histogram. This method has a high sensitivity to translation and rotation, and the convergence range of the algorithm is larger. When the structure in the foreground of the image is beyond the vision of the image, it can still achieve successful registration. Based on preserving the sensitivity of the gradient direction weighted histogram to rotation, the authors added the position information of pixel points and weighted the distance measure of the gradient direction weighted histogram with coordinate mean and coordinate variance to achieve the purpose of effectively combining spatial information and gradient information.
# An SSI-Based Solution to Support Lawful Interception
## 6 Proof of Concept
### Case Study
Indeed, such a presentation is not provided within a secure connection but is connectionless because the presentation of the \"X credential\" is required in order to establish the connection with the LEA. Once the \"X credential\" is verified by the LEA, it releases to [PERSON] the \"Y credential\" (see Figure 9b) allowing him to interact with the LEMF. Specifically, [PERSON] scans a QR code to establish a secure connection with the LEMF and provides it with a Verifiable presentation of the \"Y credential\". Then, [PERSON] obtains the data of the LI procedure from the LEMF. We conclude this section by discussing the data stored in the blockchain. Through the Indy Scan platform, it is possible to monitor all the transactions performed by an entity. Figure 10 shows a snapshot of the Indy Scan platform, which reports the transactions performed to build the necessary infrastructure for credential issuance and revocation. The fourth column of the table represents the issuer DID of the node originating the transaction, which is the agency NA. Specifically, we find the following transactions:  
1. Tx 262048 and 262052 (SCHEMA): These transactions define the structure of two credentials: Employee Credential and X Credential, both set to version 1.0. 2. Tx 262049 and 262053 (CLAIM_DEF): Claim definitions associate the schemas (Employee Credential and X Credential) with the issuer's DID. Figure 8: Overview of the X Credential issuance process (**a**) and its storage in [PERSON]’s wallet (**b**). 3. Tx 262054 (REVOC_REG_DEF): This transaction creates a revocation registry for a credential definition, specifying ISSUANCE_BY_DEFAULTAULT as the issuance type and a registry capacity of 1024 entries. 4. Tx 262055 (REVOC_REG_ENTRY): This transaction logs an entry in the revocation registry, enabling tracking and management of the revocation status for issued credentials. These transactions show the sequential process of decentralized credential management:  
* Schemas define the data structure of credentials. * Credential definitions connect schemas to issuers, facilitating the issuance of verifiable credentials. * Revocation registries manage the status of issued credentials, promoting transparency and trust in the decentralized identity ecosystem. Overall, this setup preserves the integrity of the credential lifecycle, allowing issuers, holders, and verifiers to interact securely within the Hyperledger Indy framework. Figure 9: Overview of the policy for the Y Credential (**a**) and the Y Credential itself (**b**).
# Purification of Produced Water by Solvents to Enhance Oil Recovery and Reuse Separated Droplets
## 4 Discussion
### Hydrocarbons and Chlorinated Hydrocarbons
#### 4.1.3 Chlorinated Hydrocarbons  
Incorporating chlorine atoms into hydrocarbons markedly enhances oil recovery in water purification processes. As depicted in Figure 2d, recovery rates soar to between 90% and 100%, with carbon tetrachloride being particularly notable. Dichloromethane and chloroform also show exceptional effectiveness in these processes. Despite their efficacy, the industrial application of these chlorinated hydrocarbons is advised against due to their associated risks. Tetrachloroethylene, for instance, decomposes into corrosive and toxic substances like hydrochloric acid and trichloroacetic acid in moist environments. Its violent reaction with aluminium coupled with its environmental hazards, particularly to aquatic systems, renders it a less favourable choice. Dichloromethane, which has a boiling point of 40 \({}^{\circ}\)C, is unsuitable for high-temperature applications without additional cooling, and the temperature of produced water often exceeds 45 \({}^{\circ}\)C. Chloroform, although less technologically challenging due to its higher boiling point of 61 \({}^{\circ}\)C, poses health risks including central nervous system effects and potential carcinogenicity.
## 3 Methodology
### Contextual Hypergraph Construction
#### 3.1.4 Hypergraph construction algorithm  
The hypergraph construction algorithm has been presented in Algorithm 1.  
```
0: Document \(D\) with sentences \(S=\{s_{1},s_{2},\dots,s_{n}\}\)
0: Hypergraph \(H=(V,E)\)
1: Initialize \(V\leftarrow\emptyset\)
2: Initialize \(E\leftarrow\emptyset\)
3:for each sentence \(s_{i}\in S\)do
4:\(v_{i}\leftarrow\text{Embed}(s_{i})\)\(\triangleright\) Convert sentence to embedding
5:\(V\gets V\cup\{v_{i}\}\)
6:endfor
7:for each pair of sentences \((s_{i},s_{j})\)do
8: Calculate semantic similarity \(sim(v_{i},v_{j})\)
9:if \(sim(v_{i},v_{j})>\theta_{s}\)then
10:\(e_{k}\leftarrow\{v_{i},v_{j}\}\)\(\triangleright\) Create semantic hyperedge
11:\(E\leftarrow E\cup\{e_{k}\}\)
12:endif
13:endfor
14:for each consecutive pair of sentences \((s_{i},s_{i+1})\)do
15:\(e_{l}\leftarrow\{v_{i},v_{i+1}\}\)\(\triangleright\) Create narrative hyperedge
16:\(E\leftarrow E\cup\{e_{l}\}\)
17:endfor
18:for each discourse role \(R\)do
19:\(D_{R}\leftarrow\{v_{i}\mid\text{role}(v_{i})=R\}\)
20:if \(|D_{R}|>1\)then
21:\(e_{m}\gets D_{R}\)\(\triangleright\) Create discourse hyperedge
22:\(E\gets E\cup\{e_{m}\}\)
23:endif
24:endfor
25:return \(H=(V,E)\)
```  
**Algorithm 1** Hypergraph Construction AlgorithmThis structured approach enables MCHES to effectively leverage diverse contextual relationships among sentences, enhancing the summarizer's ability to select the most informative content for inclusion in the summary.
## 6 Simulation Results and Discussion
### Performance Metrics  
The convergence metrics and diversity metrics are always both used to evaluate the performances of the multi-objective algorithms in the solution quality. Among these metrics, the general distance (GD), introduced by [PERSON] et al. [53], and spacing (SP), introduced by [PERSON] [54], are two general indexes for the convergence metrics and diversity metrics, respectively. Let \(d_{i}\) be the shortest Euclidean distance between the \(i\)th temporary nondominated solution and the Pareto front, and \(\underline{d}\) be the average sum of all \(d_{i}\) for all \(i\). The GD and SP are defined below:  
\[\text{GD}=\frac{\sqrt{\sum\limits_{i=1}^{\text{N\({}_{\text{sol}}}}{d_{i}}^{2}}} {\text{N\({}_{\text{sol}}})}, \tag{9}\]  
\[\text{SP}=.\sqrt{\frac{\sum\limits_{i=1}^{\text{N\({}_{\text{sol}}}}{( \underline{d}-d_{i})}^{2}}}{\text{N\({}_{\text{sol}}}-1}. \tag{10}\]  
The GD is the average of the sum of the squares of \(d_{i}\), and the SP is very similar to the standard deviation in probability theory and statistics. If all temporary nondominated solutions are real nondominated solutions, we have GD = 0. The solutions are equality far from \(\underline{d}\), we have SP = 0. Hence, in general, the smaller the SP is, the higher the diversity of solutions along the Pareto front and the better the solution quality becomes [13; 17; 19; 46; 47].  
The Pareto front is needed for both the GD and SP in calculating from their formulas [43; 44]. Unfortunately, it requires infinite nondominated solutions to form the Pareto front, and this is impossible to accomplish, even with exhaustive methods for the job scheduling problem of cloud computing, which is an NP-hard problem that has no guarantee of having the ability to obtain the global optimal solution under the complexity of polynomial time, and the traditional algorithms are intractable, resulting to a special optimization algorithm having to be used to reduce the system search complexity and improve the overall search quality [12; 15; 16; 17]. Rather than a real Pareto front, a simulated Pareto front is implemented by collecting all temporary nondominated solutions in the final generation from all different algorithm with different values of N\({}_{\text{sol}}\) for the same problem.  
There are 12 algorithms with three different N\({}_{\text{sol}}\) = 50, 100, and 150 for N\({}_{\text{run}}\) = 500, i.e., 12 \(\times\) 500 \(\times\) (50 + 100 + 150) = 1,800,000 solutions obtained at the end for each test problem. All temporary nondominated solutions are found from these 1,800,000 final solutions to create a simulated Pareto front to calculate the GD and SP.