esa-sceva/satcom-chunk-collection huggingface dataset:
# Harnessing FPGA Technology for Energy-Efficient Wearable Medical Devices
## 5 Power Optimization Strategies
### Minimizing Transmission Energy Costs
#### 5.2.4 Power-Aware Transmission  
Power-aware transmission refers to techniques which adjust a device's data transmission based on the current battery level to optimize power consumption. If the battery depletes unexpectedly, then critical physiological data might not be transmitted or recorded, potentially compromising patient monitoring and care. To mitigate this, the device can reduce the frequency or amount of data transmitted when the battery level is low, prioritizing critical data. For example, power usage can be adjusted by sending compressed data instead of raw data, ensuring that essential monitoring continues even with a low battery.  
The authors of [105] proposed a similar approach for \"power-aware transmission mode\" implementation, where the ECG data are compressed through a lossy approach while the residual error is stored in local memory in an entropy-encoded form. The original signal can be fully represented losslessly by combining the lossy compressed signal with the encoded residuals. The system can switch to a lossy transmit mode if the battery level is below some threshold. In the event of anomaly detection at the receiving end, the sensor node can be requested to send the error residue. This allows the original signal to be accurately reconstructed without any loss, enabling further analysis of the anomaly.  
The authors of [114] exploited a similar approach in their ECG monitoring system. A dual transmission mode strategy was employed, depending on the battery level. The transmitter within the sensor node continuously transmits raw ECG samples when the battery voltage is above a predefined threshold (referred to as the high-power mode). However, when the battery level falls below the threshold, an energy-saving mode is activated, and only critical information is sent in encrypted (3 bit) form. This helped induce a 98% reduction in transmission load and therefore power consumption compared with the high-power mode.  
However, such implementations require more resources in FPGAs and impact the device's real-time monitoring capabilities due to reduced data transmission rates. Nevertheless, these downsides are often offset by the benefits of preventing the loss of critical information, extending device life, and improving reliability.  
The benefits and constraints of these optimization techniques are summarized in Table 4 along with their descriptions.
## 3 Heavy Metal Removal Approaches
### Some Other Advanced Microbial-Based Approaches for Heavy Metal Removal
#### 3.6.2 Genetically Engineered Organisms
Finally, primary active transporters, including cdtB/Ip_3327, MntA, TcHMA3, and CopA, are also situated in the inner lipid membrane. It is noteworthy that certain transporters, such as the porin channels, may also be found in the outer lipid membrane [66,147]. This is an important consideration to keep in mind when studying the intricate mechanisms of transporter systems. Once hazardous metals infiltrate the cell, a myriad of phytochemicalins, metallothioneeins, and polyphosphates work in harmony to sequester the metals [148]. Altering the hazardous metal import-storage systems of microorganisms could potentially augment their proficiency in extracting hazardous metals from water and soil. In the ongoing battle against the deleterious compounds that pervade the environment, it has become increasingly apparent that the utilization of genetically engineered microorganisms constitutes an indispensable tool in expediting the restoration process [149]. Such organisms, endowed with the capacity to efficiently degrade and neutralize the toxic substances present in the environment, have demonstrated their efficacy in mitigating the deleterious impact of these compounds on the ecosystem [150]. Recent research has brought to light recently discovered genetic elements that play a crucial role in the degradation of a broad spectrum of hazardous material contaminants. The elucidation of these genes was made possible through the application of high-throughput and next-generation sequencing techniques [151]. In addition, the advent of innovative technologies such as CRISPR-Cas gene-editing tools has provided an avenue for the optimization of bioremediation. By employing CRISPR technology, heavy metal remediation genes can be transported to plants or bacteria and facilitate heavy metal detoxification. Metallothioneeins and phytochemicals can be synthesized more efficiently with CRISPRmediated gene expression [152]. The use of CRISPR-Cas9 gene-editing technology has been demonstrated in numerous studies to enhance metal remediation by transferring certain plant and bacterial genes. In Arabidopsis and tobacco plants, the NAS1 gene (encoding the nicotianamine (NA) synthase enzyme) has been shown to improve tolerance to harmful metals, such as Cu, Cd, Ni, Mn, Fe, Mn, and Zn [153]. By genetically modifying microorganisms with genes that are intimately involved in the breakdown of particularly stubborn substances, the bioremediation process can be vastly improved [152,154].
## 3 Results
### Cytotoxicity  
Considering the potential gastrointestinal effects of the developed CBG delivery systems, the cytotoxicity of the systems was evaluated using the intestinal cell line Caco-2 to identify the highest non-cytotoxic concentration. The results are presented in Figure 3 for CNC-CTAB encapsulating CBG (0.01-0.32 g/L), alongside the free biomolecule at equivalent concentrations to those present in the encapsulated systems, considering a 3:1 carrier-to-biomolecule active ratio (0.0025-0.08 g/L).  
Figure 3: Impact of CNC-CTAB systems encapsulating CBG and free CBG upon Caco-2 cells metabolic activity. The dotted line indicates the 30% cytotoxicity threshold as specified by ISO 10993-5:2:2009. Different letters denote statistically significant differences (\(p\) < 0.05) observed among the samples tested at each concentration.  
Figure 2: ATR-FT-IR spectra for CNC-CTAB encapsulating CBG, CNC-CTAB, and free CBG. Significant absorption bands are highlighted in orange and indicated by arrows, corresponding to specific functional groups or vibrational modes discussed in the text.  
Considering a cell cytotoxicity threshold of \(<\)30% metabolic inhibition (as per ISO 10993-5:2:2009), CBG demonstrated statistically significant reductions (\(p<\) 0.05) in cell metabolism at concentrations of 0.02 g/L (60 uM) and higher, while concentrations up to 0.01 g/L (30 uM) were found to be safe for Caco-2 cells (Figure 3). Existing literature supports a concentration-related effect of CBG on Caco-2 cells, with a decrease in cell viability observed at the highest concentration tested (30 uM) [25]. CNC-CTAB delivery systems encapsulating CBG exhibited no cytotoxic effects on Caco-2 cells up to 0.12 g/L (\(<\)30% metabolic inhibition). Consequently, these systems were able to mitigate CBG cytotoxicity toward Caco-2 cells (\(p<\) 0.05), as they maintained safe concentrations of 0.12 g/L, containing approximately 0.03 g/L of the biomolecule. This suggests a potential threefold increase in CBG dosage (from 0.01 g/L to 0.03 g/L). Utilizing encapsulation techniques to alleviate ingredient toxicity proves to be a valuable strategy for enhancing the applicability and biological functions of active biomolecules [26; 27]. The bio-compatible concentrations of each sample (0.12 g/L for the delivery system and 0.01 g/L or 30 uM for CBG) were employed for subsequent biological activity assessments.
## 2 Data and Methods
mass variations; 4) mass variations caused by degree one terms and the corresponding SLR-based \(C_{20}\) estimates together with GRACE-determined \(C_{30}\) or GSFC \(C_{30}\) are also computed for RL05 and RL06 data. For experiment 1, SW only stands for one time series of degree one terms, as the correction from degree one terms is the same for all the GRACE RL05 data published by each data processing center. SUN11 (or SUN14 or SV) actually indicates three time series of degree one terms separately corresponding to GRACE RL06 data provided by CSR, GFZ, and JPL, since the correction from degree one terms is not exactly the same for all the GRACE RL06 data published by each data processing center. We calculate the correction from degree one terms for GRACE RL06 data published by each data processing center. In experiment 2, there is only one time series of SLR-based \(C_{20}\) estimates in TN07 (or TN11, or TN14, or GFZ-SLR) for all the GRACE data published by each data processing center. Only one time series of GSFC \(C_{30}\) estimates since March 2012 are provided in TN14 and then adopted in experiment 3. GRACE-determined \(C_{30}\) solutions separately corresponding to GRACE RL05 and RL06 data published by each data processing center are analyzed in experiment 3. As for the analysis of the combination of degree one terms, SLR-based \(C_{20}\) estimates and GSFC or GRACE-determined \(C_{30}\), three cases are considered: 1) for GRACE RL05 data, SW, SLR-based \(C_{20}\) estimates in TN07 and GRACE-determined \(C_{30}\) solutions from each data processing center are used. 2) For GRACE RL06 data, SUN11, SLR-based \(C_{20}\) estimates in TN11 and GRACE-determined \(C_{30}\) solutions from each data processing center are analyzed. 3) Again, for GRACE RL06 data, SUN14, SLR-based \(C_{20}\) estimates in TN14 and GRACE \(C_{30}\) during the time span from April 2002 to February 2012, as well as GSFC \(C_{30}\) solutions since March 2012 are utilized. It should be noted that only these low degree spherical harmonics are used to calculate mass variations over both ice sheets. There are no north-south strips on the maps of low degree term-derived mass change so no Gaussian smoothing is needed. No GIA model is needed in this study, as we do not derive total mass variations over both ice sheets, avoiding the potential contamination from uncertainties of the GIA models [32] and high degree spherical harmonic coefficients in GRACE monthly gravity field solutions [33]. The study regions we choose are marked in Figure 1. We select the whole GrIS, including its surrounding small glaciers and ice caps (GICs), mostly considering that it is unlikely to separate mass variations of the ice sheet from the GICs, based on GRACE data with a relatively coarse spatial resolution of \(\sim\)3 km or longer. For Antarctica, the regions with grounded ice sheets are chosen in this study. Figure 1: Geographic locations of the study regions in red: (**a**) the Greenland ice sheet (GrIS) and (**b**) the Antarctic ice sheet (AIS).
# Watermarking Protocols: A Short Guide for Beginners
## 3 Security Primitives
### Zero-Knowledge Proof  
Zero-knowledge proof of knowledge is a basic technique that can be used by a party, called the \"prover\", to prove to another party, called the \"verifier\", that a given statement is true without providing any additional information besides the fact that the statement is indeed true [21]. This technique results in a two-party protocol involving a prover and a verifier. The protocol enables the prover to prove to the verifier knowledge of a secret about a statement without disclosing anything about the secret to the verifier. This also means that a prover without the knowledge of the secret can convince the verifier with negligible probability. Moreover, the verifier cannot learn any information about the secret during the execution of the protocol.  
More formally, the notation  
\[\mathsf{PK}\{(x):y=f(x)\}\]  
represents a zero-knowledge proof of knowledge of the secret \(x\) such that \(y=f(x)\). In particular, in the notation, the letter \(x\) in round brackets represents the secret, whereas the result \(y\) and the function \(f\) are known to the verifier [22].  
Zero-knowledge proof of knowledge has found many applications in watermarking protocols. One of the most frequent uses concerns the verification of public-private key pairs in public key infrastructures (PKIs). For example, the notation  
\[\mathsf{PK}\{(sk^{\prime}):(pk^{\prime},sk^{\prime})\gets C\leftarrow \mathsf{Enc}(pk,sk^{\prime})\}\]  
denotes a proof that \(C\) is the encryption of the secret key \(sk^{\prime}\), corresponding to the public key \(pk^{\prime}\), carried out using the public key \(pk\) and the encryption algorithm \(\mathsf{Enc}\). As a consequence, a party who possesses the secret key \(sk\), corresponding to \(pk\), can retrieve \(sk^{\prime}\) from \(C\). In this regard, to instantiate the proof, the encryption algorithms described in [23; 24] can be used.  
Other relevant uses of zero-knowledge proof of knowledge concern the need to prove that a content is the encrypted version of a binary string usually representing a fingerprint or watermark information. For example, the notation  
\[\mathsf{PK}\{(b):C\leftarrow\mathsf{Enc}(pk,b)\wedge b\in\{0,1\}\}\]  
denotes the proof that \(C\) is the encrypted version of the bit \(b\) under the public key \(pk\)[25].  
Zero-knowledge proofs of knowledge can be considered very useful for watermarking protocols since they make it possible to convince a party of something without revealing any secret information. However, they force the prover and the verifier to execute complex protocols. Therefore, according to what is reported in Section 2.1.1, these two specific roles should never be assigned to the buyers, whose involvement should be as simplified as possible. In fact, protocols such as the one proposed in [26] are characterized by security schemes that are simple but impractical in the current web context.
## 3 Results
### Genes Related to N Uptake, Transport, and Assimilation  
N metabolism in plants mainly involves three processes: N uptake, transport and assimilation. Many genes involved in NO3-/NH4+ uptake, transport and assimilation were differentially expressed in the six samples (Tables S2 and S6). In the current study, 27 DEGs encoding NO3- transporters (_NRT1_, _NRT1.5_, _NRT 1.8_, _NRT2_, _NRT3_, and _CLC-B_ (chloride channel protein B)) were detected (Figure 6, Table S7). The abundances of the transcripts of these genes, except for 1 DEG (BnaC09g20910D), were increased in CKvsNT; transcript levels of 4 genes decreased in CKvsAT (15). In NTvsAT, the transcript levels of most DEGs were reduced in both 15 and 814, but 2 DEGs (BnaC09g20910D and BnaC02g36720D) were upregulated only in 15. A total of 5 genes encoding NH4+ transporters were identified, of which BnaCrng01740D was responsive only in 15 among the six samples (Table S7). Transcript levels of 3 DEGs were increased in CKvsNT (15). Two DEGs were upregulated in CKvsNT (814), though the other 2 DEGs were downregulated (Figure 6). One DEG (BnaA01g23190D) was upregulated in NTvsAT (814). Two DEGs were downregulated in NTvsAT (15 and 814).  
Some of the DEGs encode key enzymes in NO3-/NH4+ assimilation, including 4 DEGs encoding nitrate reductase (_NR_), 10 DEGs encoding cytochrome B5 (_CB5_) of _NR_, 2 DEGs encoding nitrite reductase (_NIR_), 5 DEGs encoding glutamine synthetase (_GS_), and 5 DEGs encoding glutamate dehydrogenase (_GDH_) (Figure 6, Table S7). In CKvsNT, there were more upregulated DEGs than downregulated DEGs encoding _NR_, _CB5_, _NIR_, and _GS_ in the two cultivars. In CKvsAT, there were more downregulated DEGs than upregulated DEGs encoding _NR_, _CB5_ and _GS_ in the two cultivars. In NTvsAT, 1 DEG encoding _NR_, 5 DEGs encoding _CB5_, and 1 DEG encoding _NIR_ were downregulated; 1 upregulated DEG and 1 downregulated DEG encode _SS1.3_ and _GS2_, respectively (Table S7). Interestingly, DEGs encoding _GDH_ were always downregulated in CKvsNT and upregulated in NTvsAT.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Comparison Groups**} & \multicolumn{2}{c}{**SSM**} & \multicolumn{2}{c}{**FAB**} & \multicolumn{2}{c}{**RIB**} & \multicolumn{2}{c}{**CWB**} \\ \cline{2-9}  & **Up** & **Down** & **Up** & **Down** & **Up** & **Down** & **Up** & **Down** \\ \hline CKvsNT(15) & 158 & 20 & 28 & 1 & 481 & 1 & 8 & 0 \\ CKvsNT(814) & 142 & 13 & 21 & 1 & 566 & 1 & 6 & 0 \\ CKvsAT(15) & 18 & 20 & 3 & 1 & 6 & 29 & 0 & 0 \\ CKvsAT(814) & 4 & 8 & 0 & 2 & 0 & 9 & 0 & 0 \\ NTvsAT(15) & 19 & 161 & 0 & 32 & 2 & 613 & 0 & 9 \\ NTvsAT(814) & 16 & 162 & 0 & 22 & 4 & 631 & 0 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Numbers of up- and downregulated genes in starch and sucrose metabolism (SSM), fatty acid biosynthesis (FAB), ribosome (RIB), and cell wall biogenesis (CWB) in the six comparison groups.
# Improving the Efficiency of Energy Consumption in Buildings: Simulation of Alternative EnPC Models
## 2 State of the Art
### Shared Savings Contracts  
In Shared Savings Contracts, ESCOs are responsible for the design, implementation, and financing of the project, and a percentage of pre-established energy savings are shared between public and private partners during a fixed contractual period [18]. The performance in this type of contract is related to the percentage of energy cost savings. Based on the cost of the project, the duration of the contract, and the risks assumed by ESCO and the client, a percentage of the cost savings is predetermined, which is divided by the two actors over a previously defined period of time [29]. In a standard shared savings agreement, in addition to financing, project development, and implementation of performance risks, ESCOs are also responsible for interest rate risk and the risk of increased service costs, as well as for the adjustment clause agreed by both parties [17].  
In addition to being a good introductory model in the development of ESCO markets, the fact that the client has no associated financial risks, being only obliged to pay a percentage of the actual savings to the ESCO for a specified period of time, which is not considered a debt and therefore does not appear on the client's balance sheet, are two of the main advantages of this type of contract [29]. Thus, unlike the first alternative acquisition model mentioned, the shared savings model can be considered \"off-balance\" in public accounts if operational and performance risks are fully allocated to the private partner [18].  
However, the possibility for ESCO to become too indebted to these projects, creating difficulties in obtaining reasonably priced financing and in the contraction of new splits on additional projects, represents a disadvantage regarding Shared Savings Contracts [29].  
Observing Figure 2, which shows the relationships between the various actors participating in Shared Savings Contracts, it can be seen that, unlike Guaranteed Savings Contracts, there is no interaction between the client and the financial institution in this type of contract.  
Figure 2: Shared Saving Contract (adapted from [28]).  
According to [30], the ESCO market has changed over the last few decades as a result of the preference for Guaranteed Savings Contracts over Shared Savings Contracts, and in 2017, the guaranteed savings model was used in 86% of the EnPCs. The main reasons for this change are directly related to greater certainty of savings, lower financing costs, and lower transaction costs of Guaranteed Savings Contracts, from the owners' perspective [31].
## 4 Outdoor Activities in the Jerte Valley: Canyoning and the 2017 Flash Flood
### The 2017 Flash Flood in Jerte Valley
Despite the high precipitation values, most of the rainfall occurred hours after the flash flood's arrival; thus, surprisingly, only a small amount of rain was responsible for the disaster. A question arises about the location of the rainfall station. Is it possible that the amount recorded was a fraction of the actual value? Most of the rainfall stations are located in foothills, and storms usually leave more rain over high mountains. To address this important question, the available radar data were used. The radar data (Figure 6B) confirmed the presence of moderate storm activity (maximum reflectivity of 49 dB) over the headwaters of the basin at 11 a.m. The data indicate rainfall amounts of \(\sim\)30 mm for the previous three hours (9-11 a.m. GMT + 1). The total amount of rainfall during this event slightly exceeded the two-year return period, estimated at 69 mm in 24 h in Hoyos, and 66 mm in 24 h in Papoios, but only the first three hours of the event produced the flood that reached both canyons and killed the canyoneers. Thus, this rainfall event cannot be considered exceptional, even in the higher parts of the basin, as the radar data confirm. Therefore, our first hypothesis about rainfall  
Figure 6: (**A**) Rainfall of the 2017 event at Jerte valley stations (Source: SAIH Tajo). (**B**) Radar rainfall data from 9:00 to 11:00 (GMT + 1) (source: AEMET). Colored arrows indicate the start of activity (blue) and the flood wave’s arrival at Hoyos and Papoios. underestimation is not supported by the data, and thus, other hydrological reasons must be found.
# Prediction of Critical Currents for a Diluted Square Lattice Using Artificial Neural Networks
## 2 Experimental Setup and Transport Measurements
### Measurements Using PPMS  
The patterned superconducting film with a diluted square lattice had \(T_{c}\) of 8.646 K, which is smaller than \(T_{c}\) of the unpatterned film. For transport measurements, we placed the sample in liquid helium to help reduce the heating contact. Figure 2 shows the voltage measurements at different temperatures below \(T_{c}\) and zero applied fields. Change in the slope in these IV curves suggest the existence of three regions: one where the voltage is almost zero, which gradually increases in the second region, followed by the region in which the current increases linearly with voltage. While the Shapiro steps may be clearly observed in the second region within this temperature range, they continue to weaken until completely vanishing at higher temperatures; this happens mainly due to thermal fluctuations. Our observations are in agreement with those existing in literature [25].  
For seven constant values of temperature (8.0, 8.1, 8.2, 8.3, 8.4, 8.5 and 8.6 K) and four constant values of magnetic fields (0, 100, 200 and 300 Oe), the measuring unit generated a dataset of 7 \(\times\) 4 \(\times\) 642 IV values. The next section describes the ANN methodology to be used for prediction.
# Development of Multi-Channel Seismic-Electrical Combined Rolling Coverage Measurement System
## 2 Design of the Combined Measurement System
### Design of Coverage Cable
The shield layer of the cable provides both a current return path for the geophone's output and noise shielding for the low-voltage analog seismic signal, ensuring the integrity of the signal. Additionally, the sensor signals are amplified in two stages by the instrument's analog channels, enabling the system to effectively capture weak seismic signals.