esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Hyperconverged TPM
### Anchoring of the Persistent State
#### 4.1.2 Remote Anchoring  
The components and processes involved in remote anchoring are similar to those used in local anchoring. In local anchoring, two kernel modules are employed. The first one runs on the host machine and is responsible for anchoring the m-vTPM to the physical TPM (m-vTPM-AM). The second module runs on the m-VM and anchors the u-vTPMs to the m-vTPM (vTPM-AM). Both modules receive information from the Agent-Host. The Agent-Host determines whether the measurement originates from a u-vTPM or an m-vTPM and forwards the measurements to the appropriate module.  
In the case of remote anchoring, the local host's m-vTPM-AM is not used. In this scenario, when the Agent-Host receives a measurement belonging to the m-vTPM, it forwards it to the management VM (m-VM). At this point, two processes are carried out by the m-VM. The first process involves executing an anchoring selection algorithm to decide which remote m-VM the m-vTPM will be anchored to. The second process concerns sending the respective m-vTPM measurements to the selected remote m-VM.  
The remote m-VM anchors the received measurements in its own m-vTPM using the same anchoring process as the u-vTPM described in Section 4.1.1. The only difference is that the integrity records of the persistent and volatile states (ps-IR and vs-IR) are anchored in different PCRs. This allows the attestation of the m-vTPMs to occur independently of the u-vTPM. For example, while the IRs of the u-vTPMs are anchored in mPCRs 15 and 16, those of the m-vTPM are anchored in mPCRs 17 and 18. This remote anchoring process is illustrated in Figure 8.  
The verification process of the hyperconverged TPM is carried out in three phases. The first phase involves the verification of the TPM-enabled machines. For a host with a TPM to be considered part of the pool that constitutes the hyperconverged TPM, it must be attested. Therefore, this phase consists of the attestation process of all the TPM-enabled machines in the system, aiming to ensure that the TPM chip is genuine and that the execution environment is trustworthy. If any of the machines are found to be in an untrusted state, they will be excluded from the hyperconverged TPM pool.  
The second phase refers to the integrity verification of m-vTPMs that are anchored to the TPM chip, i.e., those belonging to hosts with a TPM. This verification is performed as described in the Section Measurement Files. If an m-vTPM is deemed untrustworthy, the host to which it belongs, as well as all the hosts hosting m-vTPMs anchored to it, will be excluded from the hyperconverged TPM.  
The third phase refers to the integrity verification of m-vTPMs anchored in a remote m-vTPM. This verification is performed analogously to the verification of the u-vTPM, as described in the solution presented in Section 4.1.1. Similarly, if the m-vTPM anchored to the remote m-vTPM is found to be compromised, the machine hosting it will be removed from the hyperconverged TPM.
# New Security Improvements in Next-Generation Passive Optical Networks Stage 2 +
## 2 Related Works
The authors of [22] proposed error correction for PUFs using generalized concatenated codes. The authors of [23] explained how methods from coding theory are applied in order to ensure reliable key reproduction. The article [23] shows how codes for key reproduction in PUFs can be constructed using Reed-Muller (RM) and Reed-Solomon (RS) codes in combination with generalized concatenated codes. The authors of [24] introduced a scheme that only uses an error correcting code without any further helper data. The main idea is to construct for each PUF instance an individual code that contains the initial PUF response as a codeword. They use low-density parity-check (LDPC) codes; however, other code classes are also possible. We deal with the GPON security issue [25; 26; 27]. Our model is based on the propagation delay between the OLT and ONUs. Each ONU has a different distance from the OLT due to the different floors of each customer and the reserve of the optical fiber in a customer's flat or house. The main contribution of our article is the improvement of security in the NG-PON2 by the use of PUFs, correction codes, and more suitable cryptographic algorithms. Suitable PUFs for use in the NG-PON2 are optical and memory-based PUFs. For example, the DRAM PUF is a low-cost solution suitable to implement in an ONU. The algorithm that is suitable for the encryption of the XG-PON encapsulation method (XGEM) payload and data encryption keys is the Advanced Encryption Standard cipher-based message authentication code (AES-CMAC) with a 128-bit key. The AES-CMAC mode ensures confidentiality, authenticity, and integrity of binary data. The suitable algorithm for integrity protection and data origin verification for ONU management and control interface (OMCI) messages in NG-PON2 is AES-CMAC-64. We emphasize that there must be mutual authentication between an ONU and an OLT CT.
###### Abstract  
It is expected that the pervasive deployment of multi-tier 5G-supported Mobile-Fog-Cloudotechnological computing platforms will constitute an effective means to support the real-time execution of future Internet applications by resource- and energy-limited mobile devices. Increasing interest in this emerging networking-computing technology demands the optimization and performance evaluation of several parts of the underlying infrastructures. However, field trials are challenging due to their operational costs, and in every case, the obtained results could be difficult to repeat and customize. These emerging Mobile-Fog-Cloud ecosystems still lack, indeed, customizable software tools for the performance simulation of their computing-networking building blocks. Motivated by these considerations, in this contribution, we present _VirtFogSim_. It is a MATLAB-supported software toolbox that allows the _dynamic joint optimization_ and _tracking_ of the energy and delay performance of Mobile-Fog-Cloud systems for the execution of applications described by general Directed Application Graphs (DAGs). In a nutshell, the main peculiar features of the proposed _VirtFogSim_ toolbox are that: (i) it allows the _joint dynamic_ energy-aware optimization of the placement of the application tasks _and_ the allocation of the needed computing-networking resources under _hard constraints_ on acceptable overall execution times; (ii) it allows the _repeatable_ and _customizable_ simulation of the resulting energy-delay performance of the overall system; (iii) it allows the _dynamic tracking_ of the _performed resource allocation_ under time-varying operational environments, as those typically featuring mobile applications; (iv) it is equipped with a user-friendly _Graphic User Interface_ (GUI) that supports a number of graphic formats for data rendering; and (v) its MATLAB code is optimized for running atop _multi-core_ parallel execution platforms. To check both the actual optimization and scalability capabilities of the _VirtFogSim_ toolbox, a number of experimental setups featuring different use cases and operational environments are simulated, and their performances are compared.  
5G-supported Mobile-Fog-Cloud platforms; energy and delay-constrained mobile applications; modeling and dynamic optimization; simulation and parallel execution
## 3 Methodology
### Frequency Ratio Methods
#### 3.2.1 General Development
This cluster method for obtaining the FR intervals is a proposed improvement to the general method that is developed in this article and called the 'cluster frequency ratio' (CFR). The number of clusters should approximately coincide with the number of classes calculated for the discrete variables. Otherwise, the weights calculated for each variable would be inadequately compensated. A procedure to better capture the variation of the FR value (and avoid discrete jumps in its distribution) adjusts by regression the values of the centroids for FR of the classes obtained by the cluster grouping described according to CFR. To avoid the previously mentioned decompensation with the discrete variables, only the centroid data are used, and not the data of the original intervals. This method is called'regression frequency ratio' (RFR). The fit need not be precise; nor is it necessary to obtain a single regression curve (thereby avoiding complex polynomial equations). Accordingly, the solution adopted distributes the centroids of the available cluster classes into two groups, one lower and the other higher (with the possibility of overlap at the intermediate point). Finally, to broaden the options, the same classes defined for slopes in the ELSUS map [23; 26] in its two versions (v1 and v2) are used in this work. It is considered a suitable precedent as it is also a generalist map covering large and diverse regions. Consequently, the ELSUS classes have been used to define another FR methodology, referred to in this article as the 'ELSUS frequency ratio' (EFR), to compare with the other FR calculation methodologies described above (see Table 2). The levels consider the zone-specific landslide frequency analysis, although the detailed procedure is not specified in the text of the article. Specifically, we are going to work directly with the intervals defined in the v2 version for the Z5 zone (mountain area: temperate climate with dry summers). The number of classes for each variable can be increased at the authors' discretion, but this does not obviously improve the results. On the other hand, we understand that it is preferable that the balance of classes is equivalent among all the variables used by the model. As an example, in the study conducted by [PERSON] et al. (2020) [32] with several bivariate models, a total of 13 factors or variables were used and no continuous variable exceeded 8 classes.
# Surface Coatings and Treatments for Controlled Hydrate Formation: A Mini Review
## 4 Surface Active/Hydrate-Philic Coatings  
[PERSON] et al. [55] showed that surface-active coatings produced by silanization can promote the methane hydrate formation. The authors chose glass substrate and silanes of different chain lengths, namely, ethyltriethoxysilane (ES), _n_-dodecyltriethoxysilane (DS), and _n_-octyltriethoxysilane (OE), to create surfaces with varying hydrophobicities that may reduce the surface tension and gas-enriched layer on reactor walls to promote hydrate formation. Later, these silanized surface-active coatings were tested for their influence on the hydrate formation in stationary and dynamic conditions using a high-pressure, stirred autoclave with sight glasses for visual observations. The experiments performed in transient conditions showed almost similar trends on all silanized glasses. However, the results obtained from stationary tests clearly showed a promotion effect. The authors claimed that the distinct experimental setup was the reason for this contrasting behaviour. In stationary tests, methane was injected directly into the hydrate plug using an ascending pipe, which diffused into the methane-saturated layer at the coated-glass surface, resulting in more hydrate formation. This effect was absent during transient experiments because of the presence of a \"sealing cork\"-like hydrate plug. However, the durability of the coatings under mechanical stress needs to be assessed for their long-term usage.
## 3 Safety Evaluation in the Phase 1 Clinical Trial as PO  
The greatest advantage associated with delpazolid is its safety. In phase 1a of a phase 1 clinical trial to evaluate its safety, as illustrated in Table 1, 64 subjects were divided into eight groups, six of whom were administered delpazolid and two who were administered the placebo. The study was the first double-blind, randomized human trial of delpazolid. To deliver single-ascending-doses (SADs), delpazolid was administered in a step-wise manner from 50 mg up to 3200 mg. Only mild adverse events were observed up to 2400 mg. At a delpazolid dose of 3200 mg, gastrointestinal (GI) tract-related adverse events were noted. In the 3200 mg dose group, volunteers had to ingest 16 tablets of 200 mg delpazolid tablets at once, resulting in GI tract-related adverse events. Therefore, the maximum tolerated dose of delpazolid was determined to be 2400 mg per day.  
Figure 1: (**A**). Synthetic scheme showing that delpazolid can be synthesised in only seven steps with difluoro-nitrobenzene as the starting material. Each step shows a high yield and the products are easily purified without chromatography. The red color indicates cyclic amidrazone. (**B**). Chemical structure of linezolid.  
A phase 1b study was conducted based on multiple-ascending-doses (MADs) over seven days. Thirty-two subjects were divided into eight groups, six of whom were administered delpazolid and two of whom were administered the placebo. Subjects were given delpazolid in MADs from 400 mg BID (bis in die, twice a day) up to 1600 mg BID over seven days. Doses up to 1200 mg BID for seven days were well-tolerated with no specific adverse events observed. After the 7-day MAD study, a 21-day MAD study was conducted to evaluate bone marrow toxicity, which is one of the most critical side effects of linezolid [8]. Subjects administered 800 mg once a day (QD) to 1200 mg BID delpazolid were monitored for up to three weeks to more accurately assess adverse events such as myelosuppression, as signs such as decreased platelet count may be observed even after two weeks. As illustrated in Table 1, serious adverse events were not observed under the MAD-21-day condition. In summary, no myelosuppression-related adverse events or serious adverse events were observed in phase 1a with SADs up to 2400 mg and in phase 1b with MAD up to 1200 mg BID (2400 mg per day) for 21 days. Therefore, delpazolid does not appear to exhibit adverse events associated with repeated dosing. In addition, delpazolid did not cause CYP-mediated metabolism and cardiac repolarisation issues [6; 9; 10; 11].
## 2 Dynamic Power Management
### Dynamic Frequency and Voltage Scaling
#### 2.2.2 Dynamic Voltage Scaling (DVS)  
While reducing frequency helps to reduce power, further energy efficiency gains can be obtained by also reducing the supply voltage. Equation 2 below shows the relationship between the frequency (inverse of the delay of the critical path) and the supply voltage for a super-threshold operation [9].  
\[f=\frac{K}{L_{D}}\frac{\left(V_{DD}-V_{TH}\right)^{\alpha}}{CV_{DD}} \tag{2}\]  
Here \(L_{D}\) is the logic depth of the critical path, \(\alpha\) is the velocity saturation factor, \(K\) captures process related information like mobility, oxide thickness, _etc._ and has temperature dependence, \(C\) is the average switching capacitance in the critical path and \(V_{TH}\) is the threshold voltage. Thus, under DVS, for a reduced operating frequency, the supply voltage is correspondingly scaled to reduce both the power as well as the energy per operation, which is not achieved with just DFS [10].  
The relationship between frequency and minimum supply voltage can be precisely captured in a table for a few frequency points, to guarantee that circuit timing is met even for the worst case process and temperature conditions. When the operating frequency is changed, the table is consulted for the appropriate supply voltage value and the voltage regulators are programmed to deliver this voltage to the chip. This is the approach currently used in many commercial chips which support DVS [4]. The timescales over which dynamic voltage changes happen is largely determined by the response time of the voltage regulators, which is in the micro-second range. Care must be taken to properly sequence the changes in frequency and voltage to always guarantee correct operation [4, 7].  
Note that the supply voltage chosen ensures that the chip will operate at the target frequency, under all relevant process and temperature conditions. Critical path delays can vary by a factor of 2\(\times\) across slow to fast process conditions for the same supply voltage, leading to excess power loss when only one frequency-voltage relationship is used for DVS.  
Figure 3: A low latency frequency configuration scheme for Dynamic Frequency Scaling.
# Programmable Data Plane Applications in 5G and Beyond Architectures: A Systematic Review
## 5 Open Points and Future Research Directions
* Interoperability: Ensuring compatibility between programmable data plane devices and existing network infrastructures, protocols, and services is crucial for successful deployment. To achieve this, interoperability mechanisms must be developed that facilitate the integration of programmable devices with legacy equipment. One potential approach is to create hybrid testbed environments that combine both programmable and non-programmable devices to assess the feasibility of interoperability mechanisms. Some examples of programmable data-plane-oriented testbeds are featured in [52; 108; 109]. Another promising option is to explore emerging technologies such as digital twins [110] for evaluating compatibility and identifying potential issues before deployment in real-world network architectures. * Energy efficiency: With the growing concern for environmental sustainability, energy efficiency is becoming an essential aspect of network design for 5G and beyond technologies. Programmable data plane devices are typically implemented using power-hungry hardware such as FPGAs or ASICs. This can lead to high energy consumption and costs. However, none of the surveyed articles specifically address this vital aspect. To fill this gap in the literature, research is needed to evaluate the energetic impact of programmable devices operation and ultimately develop energy-efficient schemes that can reduce power consumption while maintaining high network performance. Although the study in [111] presents an implementation within a data center framework, its central focus lies in utilizing programmable devices to consolidate traffic and mitigate the energy consumption of servers and network components. This serves as a promising initial step that can be further extended to a telecommunication network setting.
# Electromagnetic Assessment of UHF-RFID Devices in Healthcare Environment
## 2 Materials and Methods  
The purpose of the RF EMF measurements was to assure the compatibility between wireless systems installed at a healthcare center [23; 24]. In order to analyze their use in the healthcare environment and the influence of the UHF-RFID system at the 865-868 MHz frequency range, several measurement procedures have been employed. In situ measurements are performed, among other things, to:  
* determine RF exposure levels from the Equipment Under Test (EUT) and ambient sources,
* consider applicable regulations (exposure limits), as observed or extrapolated to maximum or actual maximum levels; or
* to monitor the RF exposure levels, even if they are well below the exposure limits that apply to ensure the protection against thermal effects of EMF influence.  
In order to accomplish the objectives set for in situ measurements, it is required that frequency selective assessment be carried out whereby the contributions of relevant sources of interest to the total RF exposure will be determined, including other exposures from radio-communication systems such as mobile phones or internet access.  
The projects were subject to the two assessment procedures, i.e., the first at the Hospital Universitario de Canarias (HUC), with the measurements having been performed where the RFID devices had been installed for assessment purpose. Additional measurements were performed in Madrid, where the assessment was made in an anechoic chamber, with the aim being to examine the worst-case scenario. An ad-hoc numerical simulation study was performed by means of: (1) Specific Absorption Rate (SAR) values from exposure near MO-RFID readers, (2) distribution of exposure from AG-RFID reader by means of an in-house 3D-RL algorithm, and (3) 2D contour maps representation [25; 26].
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 2 Metal Sulfide Thin Films
### Copper Zinc Tin Sulfide Films  
The copper zinc tin sulfide (CZTS) uniform films were prepared at room temperature on glass substrates [21]. The resistivity value, band gap, mobility, absorption coefficients, and carrier density were 1.51 \(\times\) 10\({}^{2}\)\(\Omega\cdot\)cm, 1.5 eV, 0.32 cm\({}^{2}\)V\({}^{-1}\cdot\)s\({}^{-1}\), 10\({}^{4}\) cm\({}^{-1}\), and 1.28 \(\times\) 10\({}^{17}\) cm\({}^{-3}\), respectively. Based on the Raman spectroscopy and XRD studies, two peaks (287 and 336 cm\({}^{-1}\)) could be observed and the films showed tetragonal phases, respectively [22]. [PERSON] and co-workers [23] reported that the grain sizes increased with the increasing deposition cycles during the experiment. The scanning electron microscopy (SEM) results revealed the formation of porous grains in the obtained samples. Low-cost non-toxic CZTS films were produced on fluorine-doped tin oxide (FTO) glass by [PERSON] and co-workers [24]. Photovoltaic behaviors (Voc = 390 mV, Jsc = 636.9 \(\mu\)A/cm\({}^{2}\), fill factor = 0.62, power conversion efficiency = 0.396%) in a photoelectrochemical solar cell were reported. On the other hand, [PERSON] and co-workers [25] studied the influence of anionic bath immersion time on the photovoltaic characteristics. The best results were obtained for shorter immersion times (power conversion efficiency = 2.33%, short circuit current (Jsc) = 12.88 mA/cm\({}^{2}\), open circuit voltage (Voc) = 0.42 V, fill factor = 0.43).