esa-sceva/satcom-chunk-collection huggingface dataset:
# Internet of Food (IoF), Tailor-Made Metal Oxide Gas Sensors to Support Tea Supply Chain
## Appendix A
\begin{table}
\begin{tabular}{c c c} \hline
**RT** & **Compound Name** & **Abundance Mean** \\ \hline
18.334 & Ammonium acetate & 216,233 \\
18.712 & Furfural & 415,801 \\
19.127 & Octane, 5-ethyl-2-methyl- & 100,177 \\
19.789 & 2,5-Heptadiene, (E,E)- & 78,358 \\
20.498 & 1,1-Dodecanediol, diacetate & 57,452 \\
20.663 & 1-Decene, 4-methyl- & 186,582 \\
20.683 & 1-Hexanol, 2-ethyl- & 133,849 \\
21.128 & Benzaldehyde & 193,767 \\
21.270 & Decane, 3,7-dimethyl- & 126,527 \\
21.350 & Heptane, 2,3,4-trimethyl- & 67,035 \\
21.509 & 3,5-Octaiden-2-one, (E,E)- & 102,421.5 \\
21.858 & Undecane, 3,7-dimethyl- & 115,470 \\
22.150 & 1-Decanol, 5,9-dimethyl- & 92,827 \\
22.402 & Cyclobutane, 1,2-bis(1-methylether)-, trans- & 81,623 \\
22.419 & Cyclohexene, 1-methyl-5-(1-methylether))- & 98,513 \\
22.829 & 1-Isopropenyl-3-propenylcyclopentane & 124,022 \\
23.281 & 1,2-Dihydrolinalool & 101,149 \\
23.317 & Cyclopropanemethanol, alpha,2-dimethyl-2-(4-methyl-3-pentenyl)-, [1.alpha.(R*),2.alpha.]- & 113,237 \\
24.144 & 2,220,364 \\
24.430 & 2-Decenal, (Z)- & 162,906 \\
24.448 & Pentane, 1-(2,-2-dibromocyclopropyl)- & 114,649 \\
25.170 & Oxitrane, octyl- & 783,66 \\
25.384 & 5-Decen-1-ol, acetate, (E)- & 100,866 \\
25.437 & 1-(1H-Imidazole2-yl)-ethanoneanone & 92,763 \\
25.862 & 1H-Pyrrole-2-carboxaldehyde, 1-ethyl- & 56,395.5 \\
26.174 & 2-Pentanol, 3-chloro-4-methyl-, (R*,S’)-(+/-)- & 245,217.5 \\
26.420 & Butylrolactone & 60,795 \\
27.010 & Sulfurrous acid, octyl 2-propyl ester & 85,408 \\
27.028 & Sulfurrous acid, dodecyl 2-propyl ester & 73,132 \\
30.159 & 2,6-Octadiene, 3,7-dimethyl-, (Z)- & 928,389 \\
30.420 & 2,5-Dihydroxyheptane & 73,287 \\
30.480 & Oxetane, 2-ethyl-3-methyl- & 81,149 \\
30.640 & Thiophene, 3-methylsulfonyl- & 69,756 \\
30.909 & p-Mentha-1(7),8(10)-dien-9-ol & 143,103 \\
31.487 &.alpha.-Terpineol & 408,707 \\
32.192 & (\(\cdot\))-Carvone & 402,266.5 \\
33.032 & 2,6-Octadiene1, 3,7-dimethyl-, (Z)- & 1,115,315.5 \\
33.362 & 167,765.5 \\
33.670 & Cyclopropanemethanol, 2-isopropylidene-alpha-methyl- & 55,873 \\
33.949 & cis-p-Mentha-2,8-dien-1-ol & 843,89,5 \\
34.711 & 2,6-Octadiene1-ol, 2,7-dimethyl- & 253,900 \\
34.970 & Cyclohexene, 3-acetoxy-4-(1-hydroxy-1-methyl)- & 68,155 \\
35.301 & Citronell & 77,794.5 \\
35.581 & p-Mentha-1(7),8(10)-dien-9-ol & 51,607 \\
36.309 & beta.-Myrence & 76,695.5 \\
36.649 & 1-Heptadec-1-ynyl-cyclohexanol & 86,850 \\
36.697 & 5-Isopropenyl-1,2-dimethylcyclohex-2-enol & 50,040 \\
37.261 & trans-p-mentha-1(7),8-dien-2-ol & 262,206 \\
37.270 & Carveol & 221,405 \\
37.820 & Butanoic acid, 3-methyl- & 378,023.5 \\
37.943 &.beta.-Myrence & 279,721 \\
38.222 & 2,5-Pyrrolidineone, 1-ethyl- & 221,432.5 \\
38.401 & Benzyl alcohol & 196,719 \\
38.417 & Benzene, [(2-propenyloxy)methyl]- & 158,666 \\
38.773 & 3-hydroxy-2-methyl-5-(prop-1-en-2-yl)cyclohexanone & 108,854 \\
39.288 & Phenylethyl Alcohol & 64,612 \\
39.733 & Acetic acid, 6,6-dimethyl-2-methylene-7-(3-oxobutylidene)oxepan-3-ylmethyl ester & 71,831 \\ \hline \end{tabular}
\end{table} 
Table 10: _Cont._
# Elliptic-Curve Crypto Processor for RFID Applications
### Numerical Flavor of the Results  
The proposed accelerator architecture over \(GF(2^{163})\) is modeled in Verilog using the Vivado design tool. To evaluate the performance, we have synthesized our design with the integration of a schoolbook polynomial multiplier (in two different logic styles, i.e., combinational, and sequential) on three different 7-series FPGA devices, i.e., Kintex-7, Artix-7 and Virtex-7. The combinational implementation of a schoolbook multiplier in our proposed accelerator architecture results in power-optimized (161 \(\upmu\)W on both Kintex-7, Artix-7 and 183 \(\upmu\)W on Virtex-7) values with an expense of higher hardware resources (3561 look-up-tables (LUTs) and 1527 flip-flops (FFs) for all the three devices). Moreover, on both Kintex-7 and Virtex-7 devices, the achieved clock frequency is 227 MHz and the time to compute one PM operation (latency) is 11.57 \(\upmu\)s, respectively. These values on Artix-7 are 192 MHz and 13.68 \(\upmu\)s, respectively. On the other hand, the sequential implementation of a schoolbook multiplier in our proposed accelerator architecture results in low-latency (2.88 \(\upmu\)s on both Kintex-7, Virtex-7 and 3.41 \(\upmu\)s on Artix-7) and area-optimized (1786 LUTs & 1855 FFs for all the three devices) values with an expense of power (647 \(\upmu\)W on Kintex-7, Artix-7 while on Virtex-7, the consumed power is 733 \(\upmu\)W). Furthermore, on both Kintex-7 and Virtex-7 devices, the achieved clock frequency is 909 MHz while on Artix-7, 769 MHz is achieved.  
It is important to note that the sequential implementation of a schoolbook multiplier allow us to achieve the objective of this paper (a low-latency (high-performance) and low-area architecture for RFID applications). Consequently, the state-of-the-art ASIC architectures, reported in [14; 19; 20; 21] over \(GF(2^{163})\) require 84,722, 11,041.6, 12,1736.1 and 190,208.3 times higher computation time as compared to our high-performance architecture synthesized on a modern Kintex-7 FPGA. Furthermore, the architecture of [22] over \(GF(\mathbb{P}_{192})\) requires 868 times higher computation time as compared to our high-performance implementation on Kintex-7 over \(GF(2^{163})\). As compared to the FPGA implementations of [18; 23; 24; 25; 26], our high-performance implementation utilizes lower hardware resources and takes lower computation time for the computation of one PM operation. Our obtained results (for high-performance and power-optimized) make our proposed architecture well-suited for RFID applications.  
The remainder of this article is structured as follows: the background knowledge for the architecture of RFID tag chip and PM computation over \(GF(2^{m})\) is presented in Section 2. Section 3 reports the proposed architecture. Section 4 summarizes the implementation and comparison to state-of-the-art details. Section 5 concludes the paper.
# A Review of Indoor Air Quality in Social Housing Across Low- and Middle-Income Countries
## 3 Results of the Literature Survey  
The article selection process identified 43 articles, as shown in Table 2, each focusing on various aspects of IAQ, which allowed these studies to be categorized into five distinct themes to align with the review's objectives. The first theme, Direct Measurements (DMs), includes research that provides quantitative data on major pollutants and their sources within social housing environments. The second theme, Health and Comfort (H&C), looks at how poor IAQ affects residents' health and well-being and focuses on problems like respiratory issues and discomfort. The third theme, Retrofit and Maintenance (R&M), examines interventions such as ventilation upgrades, energy-efficient retrofits, and improvements to building design. These studies assess the impact of such changes on air quality and living conditions. The fourth theme, Comparative Studies (CS), explores differences in IAQ across housing types and regions and offers perspectives on how structural and environmental factors shape IAQ. Lastly, Housing Conditions and Socio-Economic Factors (HC&SeFs) focuses on how building features and resident behaviors, such as overcrowding and cooking practices, contribute to IAQ problems. This categorization strategy focuses on the complexities of IAQ problems and interventions in the chosen locations. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Category** & **Reference** & **Year** & **Location** & **Study Aim** \\ \hline \multirow{8}{*}{**DMs (n = 4)**} & \multirow{4}{*}{[PERSON] et al. [31]} & \multirow{4}{*}{2022} & Bangladesh; India; China; Colombia; Ethiopia; Nigeria; Malawi; Tanzania and Kenya & Analyze PM2.5/PM10 levels in kitchens across 12 cities, emphasizing low-income households. \\  & & & & \\ \cline{1-1} \cline{2-4}  & \multirow{4}{*}{[PERSON] et al. [32]} & \multirow{4}{*}{2023} & \multirow{4}{*}{Xiamen, China} & Investigate PM2.5 levels and ventilation in Xiamen’s low-price rental housing. \\ \cline{1-1} \
## 2 Materials and Methods
### Body Composition Analysis  
The InBody 770 analyzer uses segmental multi-frequency bioelectrical impedance analysis to measure body composition by sending low-level electrical currents through the body and assessing the resistance in various tissues. It measures LBM and FM for total body, trunk, and arm both in percentage and kilograms, FMI, FMI, phase angle, visceral FM in squared cm, PBF in percentage, as well as total body BCM, SMM, and FFM in kilograms in both sexes (Biospace Co., Seoul, Republic of Korea) as shown in validation studies with DXA, where a 98% correlation between the two techniques was achieved [30]. To obtain reliable results, the measurements were performed under controlled conditions. Participants rested in the morning without being physically active in the eight hours before the measurement, did not consume any significant amount of water or food in the three hours before the examination, stood barefoot on the pedal plate electrode, and held the hand electrode at a 15\({}^{\circ}\) angle to avoid contact between the arm and torso. The InBody 770 integrated scale measured the body weight to the nearest 0.1 kg.  
The study sample was categorized by sex and obesity status (PBF \(\geq\) 28 for women; PBF \(\geq\) 20 for men) using the cutoff points provided by the InBody 770 analyzer in the data output. We used the PBF to define obesity instead of the traditional BMI. The PBF provides a more accurate assessment of obesity than BMI, which cannot distinguish between SMM and FM, potentially categorizing muscular individuals as overweight or obese and individuals with low muscle mass as healthy [31].
# A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors
## 3 Communication Optimization  
In this section, we first apply the Stochastic Gradient Descent algorithm (SGD), which is widely used in large-scale machine learning [37], to analyze the feasibility of SSP in theory, and obtain the DEFT of the iterative-convergence algorithm. Then, we introduce the improvement of SSP in detail, and propose a parameter communication optimization strategy, called DSP, based on the DEFT. Finally, we present a distributed machine learning system in sensors using DSP and the idea of parameter server based on Caffe [19].
# Refractive Indices of Ge and Si at Temperatures between 4-296 K in the 4-8 THz Region
## 4 Conclusions  
We have measured refractive indices of high resistivity Si and Ge at temperatures between 4-296 K in the frequency range between 4.2-7.7 THz. Si and Ge are two of the most technologically important semiconductors, which are also widely used in cryogenic environments such as quantum research. To our knowledge, this is the first report of refractive indices of Si and Ge at THz frequencies and at cryogenic temperatures. The frequency region addressed in this study is a particularly difficult region of the terahertz spectrum, where other topical techniques such as THz-TDS struggle to obtain high signal-noise ratio, and cryogenic measurements are quite uncommon. While the measurement  
Figure 2: Temperature dependence of the refractive index in (**a**) Ge and (**b**) Si. The refractive index values are mean averages over 4.2-7.7 THz. (The errors, taken as standard deviation of the mean refractive index, are smaller than the size of the symbols.) Solid lines are a fit to Equations (1) and (2); the fitting parameters are given in Equations (3) and (4).  
technique we used has been previously reported [14], we have combined elements of that toolbox in novel ways to overcome some of its inherent limitations. Our measurements show a systematic change in refractive index as a function of temperature. We propose a phenomenological model that describes the observed behaviour and allows one to interpolate the data.  
Conceptualisation, all authors; methodology and investigation, G.M.; software and data curation, S.C.; formal analysis, S.C. and M.N.; validation, S.C., M.N., and B.M.; writing--original draft preparation, S.C. and M.N.; writing--review and editing, M.N. and B.M.; visualisation, M.N. All authors have read and agreed to the published version of the manuscript.  
Funded by the Engineering and Physical Sciences Research Council (EPSRC), UK, under grant [COMPASS/ADDRFSS, Grant No. EP/M009564/1].  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.
# Constructed Wetlands for the Wastewater Treatment: A Review of Italian Case Studies
## 2 Classification of CWs
### Sub-Surface Flow (SSF) CWs
#### 2.2.2 Vertical Sub-Surface Flow (VSSF) CWs  
The wastewater in VSSF CWs moves vertically either as an up-flow or downflow [39] movement. In a downflow movement, wastewater is applied intermittently (with filling and draining) and it inundates the surface before entering the system through gravity [40; 41]. As wastewater passes through the medium (substrate), air enters the pores and facilitates the nitrification process [42], hence improving pollutant removal efficiency. This process can be further improved by inserting aeration pipes in the system [35]. Clogging in this system may be caused by degraded macrophytes, pollutants, and particles in the system affecting the hydraulic conductivity that influences the treatment process [43]. VSSF CWs are well-erated (aerobic condition); therefore, ammonia nitrogen is removed through the nitrification process but not nitrate nitrogen because of the absence of denitrification [15]. According to [25], TSS, BOD\({}_{5}\), and COD removal efficiencies for VSSF CWs were found to be 81.8%, 80.0%, and 78.7%, respectively.
###### Abstract  
Continuous accurate positioning is a key element for the deployment of many advanced driver assistance systems (ADAS) and autonomous vehicle navigation. To achieve the necessary performance, global navigation satellite systems (GNSS) must be combined with other technologies. A common onboard sensor-set that allows keeping the cost low, features the GNSS unit, odometry, and inertial sensors, such as a gyro. Odometry and inertial sensors compensate for GNSS flaws in many situations and, in normal conditions, their errors can be easily characterized, thus making the whole solution not only more accurate but also with more integrity. However, odometers do not behave properly when friction conditions make the tires slide. If not properly considered, the positioning perception will not be sound. This article introduces a hybridization approach that takes into consideration the sliding situations by means of a multiple model particle filter (MMPF). Tests with real datasets show the goodness of the proposal.  
Keywords:positioning; navigation; data fusion; odometry; particle filter; multiple-model filter +
Footnote †: journal: Applied Physics Letters
## 2 Materials and Methods
### Calculation of the Attenuation  
When the guided wave spreads through a thin-walled structure, the main reasons for the signal loss are geometrical spreading, material absorption and scattering and amplitude reduction from dispersion due to frequency dependency of wave speed. Since the signal loss from dispersion is considered in the block of the modal analysis, and geometrical spreading does not cause frequency dependent waveform variation, these attenuation components can be excluded from consideration. In this study, structural carbon steels with finely dispersed structures are considered. In these materials, the scattering coefficient is negligible compared to the absorption coefficient, so the main reason for the signal loss that should be considered when modeling is absorption.  
Since the main type of AE waves are Lamb waves, it is necessary to consider that the presence of dispersion significantly affects the behavior of the attenuation coefficient for these modes. The attenuation coefficient of the Lamb waves is a linear combination of the attenuation coefficients of the longitudinal wave \(\alpha\) and transverse wave \(\beta\), respectively [18]:  
\[\left\{\begin{array}{l}\gamma_{S0}=A_{S0}\alpha+B_{S0}\beta\\ \gamma_{A0}=A_{A0}\alpha+B_{A0}\beta\end{array}\right., \tag{12}\]  
where \(A_{S0}\), \(B_{S0}\), \(A_{A0}\) and \(B_{A0}\) are coefficients that are determined by the type of Lamb wave. The graphs in Figure 1a show that \(A_{S0}\), \(B_{S0}\), \(A_{A0}\) and \(B_{A0}\) significantly depend on the _f-h_ parameter.  
The energy loss can be described by the transmission coefficients \(K_{A0}\), \(K_{S0}\), which correspond to the exponential dependence on length and frequency  
\[K_{A0}(f)=e^{-\gamma_{A0}\cdot f\cdot l},\ K_{S0}(f)=e^{-\gamma_{S0}\cdot f\cdot l}, \tag{13}\]  
where \(\gamma_{A0}\) and \(\gamma_{S0}\) are the attenuation coefficients for the Lamb wave's zero-modes, and \(l\) is the propagation distance. The dependences of the transmission coefficients on parameter \(f\cdot h\) are shown in Figure 1b.
## 5 Conclusions  
* Two different forest ecosystems growing in contrasting habitats in interior Siberia were studied: a _Pinus sylvestris_ forest growing on warm sandy soils and a _Larix gentilii_ forest growing on permafrost soils with a shallow active layer depth. These forest ecosystems differ distinctively in their structure (age, height and diameter, LAI, stem density, etc.). The permafrost plays a double role: on the one hand, it supports the forest existence in a dry climate over East Siberia, delivering additional water from thawing permafrost; and on the other hand, much available energy, up to 30%-50%, is consumed in thawing ice. Thus less energy remains for sensible heat and latent heat flux, warming the soil and ambient air and for physiological processes in ecosystems.
* Net radiation was 2-2.5 fold greater in the pine forest than in the larch forest due to a 2.5 week longer growing season. Sensible and latent heat partitioned from R\({}_{n}\) and expressed by the Bowen ratio showed that \(\beta\) remained at 1-2 for the growing season when the pine forest was physiologically active and increased up to 8-10 when it was not.
* Precipitation and evaporation in the pine forest were 30%-50% greater than in the larch forest. In both ecosystems, the water balance was positive for the growing season; however, the monthly and cumulative daily water balances were often negative.
* Daily maximal half-hourly CO\({}_{2}\) fluxes were about the same in both ecosystems \(--10\) umol m\({}^{-2}\) s\({}^{-1}\). However, averaged daily CO\({}_{2}\) fluxes in the pine forest were three times larger than the fluxes in the larch forest, which resulted in 228 g C m\({}^{-2}\) season\({}^{-1}\) vs. 83 g C m\({}^{-2}\) season\({}^{-1}\), respectively. The NEP patterns in both ecosystems exposed a strong signal of them being a C-sink for the growing season and year-round. Both R\({}_{\rm eco}\) and GPP were 2-3 fold lower in our Gmelin larch.
* Water use efficiency (GPP/E) of the pine ecosystem appeared to be, on average, 2 times greater: 11 vs. 6 mg CO\({}_{2}\) g\({}^{-1}\)H\({}_{2}\)O in the larch ecosystem. Thus the water cost per unit of C-assimilation was twice greater in the permafrost larch ecosystem.
## 3 Methods
### Risk Assessment System  
After obtaining the location of moving or static objects as results of object recognition and tracking module, risk assessment module estimates the time-to-collision for each object. In this estimation, the velocity of each object is considered to be constant and the shortest distance of the closest point of the object to the test vehicle is detected by laser scanning sensor. Thus, time-to-collision and predicted collision place are estimated by using the knowledge of relative velocities and the shortest distance. However, since objects can change their motion, these estimations cannot be exact and the distribution of likely future states of the objects must be taken into consideration. Since pedestrians, unlike vehicles, are capable of making quick maneuvers, risk assessment module has extra time tolerance after the acoustic warning for pedestrians when compared to the vehicles.  
In this study, the screen display obtained from the laser scanner is divided into two zones when considering the tram's maximum velocity, dimensions, and route. The laser scanner scans the area with an angle of 110\({}^{\circ}\). As seen in Figure 6, Zone 1 is the \"Danger Zone\", whereas Zone 2 is the \"Safe Zone\". Collision probability depends on not only the zone but also the type and velocity of the object. Three different collision scenarios are evaluated in the designed system. These scenarios are as follows:No Collision Probability:  
The scenario in which there is no probability of a collision when considering the heading, velocity, position, and distance of the objects that are detected on the tram line or in the vicinity of the line. In this case, shown in Figure 7, there is no need for warning signals or brake actuation.  
2 Collision Probability:  
This scenario is divided into two cases. In the first case, the detected object is in the Safe Zone but getting closer to the Danger Zone depending on its velocity and heading. In the second case, although the object is in the Danger Zone, it is moving in the same direction as the tram and slower than the tram. In this case, an exclamation mark appears on the screen and an alarm signal is played (Figure 8).  
3 Collision Highly Probable:  
The scenario in which the probability of collision is very high considering the position, velocity, heading, and distance of the detected object. In this case, a stop sign appears on the screen, an alarm signal plays and automatic brakes are activated if the tram driver does not respond in two seconds (Figure 9).  
Figure 6: Dangerous areas and obstacles (static and moving) in an observed scene.  
Figure 8: There is an object in danger zone and getting closer to tram slowly. An exclamation mark appears on the screen and an alarm signal is activated. Heading of moving object can be seen with a red line and possible collision position can be seen as a yellow circle on the Figure 8.  
Figure 7: All objects are in the safe zone and there is no collision probability considering heading, velocity, and position of objects.
# An Accelerated Method for Protecting Data Privacy in Financial Scenarios Based on Linear Operation
## 3 Proposed Methods
### Model Validation
#### 3.3.2 Protection of Raw Data  
To protect the privacy of client data, we use a scalar \(\alpha\) to encrypt each transmitted data, while the corresponding decryption scalar \(\alpha^{-1}\) is stored only on the client side. This method of encryption necessarily results in some additional overhead:  
1. We have encryption and decryption overhead.
2. We have transmitted data overhead.
3. We have validation intermediate result overhead.  
However, since the server only computes the encrypted linear operations, it needs to communicate with the client when non-linear operations are involved, return the intermediate results to the client for decryption, and then encrypt the new intermediate results back to the server. As a result, the server has no access to the original file, which protects the privacy of the user's data. It is important to recognise that these overheads can cause delays in the creation of this file, which we will discuss in Section 5.1.