esa-sceva/satcom-chunk-collection huggingface dataset:
# Temporal-Guided Label Assignment for Video Object Detection
## 3 Methodology
### Feature Instructing Module
#### 3.3.1 Relation Module  
The relation of feature maps \(F_{t}\) and \(\left\{F_{t^{\prime}}\right\}_{t^{\prime}\in V}\) is utilized to guide the fusion of weight maps \(w^{+}_{t,i}(w^{-}_{t})\) and \(\left\{w^{+}_{t^{\prime},r}\right\}_{t^{\prime}\in V,r=g(t,i,t^{\prime})}\). Feature maps of the target frame and support frame set are separately noted as \(F_{t}\) and \(\left\{F_{t^{\prime}}\right\}_{t^{\prime}\in V}\). To calculate the similarity of the two feature maps, we realize the inner product of their corresponding positions by multiplying the two matrices. For each \(F_{t^{\prime}}\) and \(F_{t}\), the similarity map \(Z_{t^{\prime}}\) can be expressed as follows:  
\[Z_{t^{\prime}}=Z(F^{\star}_{t},F^{\star}_{t^{\prime}}):\mathbb{R}^{(H\times W )\times C}\otimes\left[\mathbb{R}^{(H\times W)\times C}\right]^{T}\rightarrow \mathbb{R}^{H\times W\times H\times W} \tag{3}\]  
where \(F^{\star}\) denotes the feature maps normalized along the channel dimension and \([\cdot]^{T}\) denotes the transposition of the matrix. \(\otimes\) denotes the matrix multiplication. Then, for each location \(\text{u}\in\left\{(1,1),(1,2),\cdots,(H,W)\right\}\) in \(F_{t}\), we select the most similar \(K\) locations in \(F_{t^{\prime}}\) by the similarity map to generate feature point scores \(z_{t^{\prime}}(u,k)\):  
\[z_{t^{\prime}}(u,k)=\frac{Z_{t^{\prime}}(u,k)}{\sum_{j=1}^{K}Z_{t^{\prime}}(u, j)} \tag{4}\]  
Figure 3: Temporal-guided label assignment framework. ‘More information’ can be anything that comes from different frames. The fused \(\widehat{w}^{+}_{t,i}(\widehat{w}^{-}_{t})\) act on the learning task of the region proposal network (RPN).  
where \(k\) denotes one specific location in the top \(K\) locations. The feature point scores are calculated by the normalization of the similarity map.
## 8 Challenges and Future Directions
### Future Directions
#### 8.2.3 Multimodality  
The combination of data from different sensors (movement data, physiological data, and context data) can create a more robust picture of human activity and highlight the fine-grained differences between similar activities. This is also important when recognizing complex activities which may have similarities in terms of body movement. Combining different modalities provides complementary information for a more robust system. Thiscan be used in healthcare applications of HAR, where an addition of physiological data can help differentiate activities like a fall from the patient lying down. Also, context data such as underlying medical conditions can also provide a richer understanding of certain activities.  
Future research should focus on addressing the challenges involved in building robust multimodal HAR systems. Some of these challenges include the increased computational cost of developing multimodal systems, the availability of multimodal data, and the synchronisation of multimodal datasets.
# On Improving Reliability of SRAM-Based Physically Unclonable Functions
## 4 Analysis of the Proposed Counter Based Design
### UP/DOWN Counter vs. TMV  
In Figure 7, the comparisons of error rates after using the TMV and UP/DOWN counter scheme are plotted against the initial error rate of the cell. Analytical results were used for both schemes. The expression for error rate of TMV can be found in related publications [10]. For comparison, the error rate reduction by using \(4-,5-\) and 6-bit TMV counters and UP/DOWN counters are plotted. For the target error rate of the order of \(10^{-6}\), the new voter design offers a significant advantage over a traditional TMV. The 4-bit UP/DOWN counter is capable of handling \(2\times\) the initial error rate compared to a 4-bit, 15-way TMV to get a final error rate \(\leq\)\(10^{-6}\). Similarly, 5-bit and 6-bit UP/DOWN counters offer \(2\times\) and \(1.8\times\) improvement. We also note that, for a 5-bit UP/DOWN counter, the counter still outperforms a 6-bit TMV counter. Hence, we expect an \(n\)-bit UP/DOWN counter to do better than an \(n+1\)-bit TMV counter for \(n\geq 5\). Another point to mention is that the improvement in reliability when using an UP/DOWN counter is a conservative estimate, as Label (3) is an upper bound, and, in reality, better gains are expected. The UP/DOWN counter has an area penalty of \(\sim\)10% to \(\sim\)15% compared to a similar simple counter used by TMV in 45 nm technology Nangate open cell library [29]. This area increase is acceptable for the significant improvement offered in error rate reduction.
## 2 Main Factors Affecting Spectral Reflection Characteristics of Leaves
### Pigments--Visible Light  
In the visible band (400-700 nm), the reflectance (measuring less than 15%) and transmittance of leaves are both very low. The main part of the solar radiation on leaves is absorbed by pigments (e.g., chlorophyll, cyanophyll, carotenoid, erythromycin, and lutein), which have reflection peaks at different wavelengths. The pigments in healthy green leaves are mainly the chlorophyll in chloroplasts [28]. Chlorophyll has an extremely high absorption rate for ultraviolet and purple light, and it strongly absorbs blue and red light. It partially absorbs and reflects green light. Therefore, the reflection spectral curves of various green plants in the visible light band are at very low levels, and all show a weak characteristic peak around 550 nm (green light), with relative intensity of only 5% to 10%.
###### Abstract  
The main goal of this study was to screen different lignocellulosic materials for their ability to support the cell growth of the yeast _Komagataella pastoris_ and the production of xylitol. Several lignocellulosic materials, namely banana peels, brewer's spent grains (BSGs), corncobs, grape pomace, grape stalks, and sawdust, were subjected to dilute acid hydrolysis to obtain sugar rich solutions that were tested as feedstocks for the cultivation of _K. pastoris_. Although the culture was able to grow in all the tested hydrolyses, a higher biomass concentration was obtained for banana peels (15.18 \(\pm\) 0.33 g/L) and grape stalks (14.58 \(\pm\) 0.19 g/L), while the highest xylitol production (1.51 \(\pm\) 0.07 g/L) was reached for the BSG hydrolysate with a xylitol yield of 0.66 \(\pm\) 0.39 g/g. Cell growth and xylitol production from BSG were improved by detoxifying the hydrolysate using activated charcoal, resulting in a fourfold increase of the biomass production, while xylitol production was improved to 3.97 \(\pm\) 0.10 g/L. Moreover, concomitant with arabinose consumption, arabitol synthesis was noticed, reaching a maximum concentration of 0.82 \(\pm\) 0.05 g/L with a yield on arabinose of 0.60 \(\pm\) 0.11 g/g. These results demonstrate the feasibility of using lignocellulosic waste, especially BSG, as feedstock for the cultivation of _K. pastoris_ and the coproduction of xylitol and arabitol. Additionally, it demonstrates the use of _K. pastoris_ as a suitable microorganism to integrate a zero-waste biorefinery, transforming lignocellulosic waste into two high-value specialty chemicals with high market demand.  
xplicit; arabitol; lignocellulosic; hydrolysis; _Komagataella pastoris_
# Compression of GNSS Data with the Aim of Speeding up Communication to Autonomous Vehicles
## 2 Background
### NMEA Standard  
The NMEA (National Marine Electronics Association) standard, also known as Standard 0183, was introduced in 1983 as a standard for data communication between ships. The NMEA protocol uses ASCII codes, and the data transfer is slow at 4800 bytes per second. However, it is still widely used and is perfectly suited to situations where one end, such as a GNSS device, needs to be connected to another end, such as a satellite [24; 25].  
The default transmission rate of the NMEA GNSS standard is 4.8 kb/s. It uses 8 bits for the data of ASCII characters and 1 stop bit. More than a few years later, the NMEA2000 protocol, much more advanced than the previous one, was invented. The new protocol allows multiple units to transmit and receive data simultaneously; its cables are less sensitive to noise (with wired connections) and its information transfer is superior to that of NMEA0183.Furthermore, it allows data transfer rates up to 250 kb/s (about 50 times faster) [6].  
The use of NMEA in ships is very significant and important because in the sea we do not have signs, and one of the options to navigate at sea in the modern world is through GNSS, in contrast with other transportation such as vehicles that can navigate the roads thanks to road signs and directions.  
Most systems that provide real-time placement ensure that the data are in NMEA form. These data include, among others, PVT: Position, Velocity and Time [26]. Most often we will see standard NMEA sentences in any GNSS devices commercial production [27]. It is also possible to define unique, proprietary sentences for a particular purpose instead of existing ones. For example, a Garmin sentence would start with PGRM [28]. We discuss standard sentences below, and all the sentences have something in common.  
Each NMEA sentence is represented by ASCII codes, starting with a $ sign and a prefix of \"GP\", which defines GNSS receivers. Three letters mark this type of sentence [29].  
For example, most popular types:  
* GPGGA--fix information
* GPGSV--detailed satellite data
* GPRMC--recommended minimum data for GNSS.
* GPGSA--overall satellite data  
Each NMEA sentence type can contain no more than 80 characters of plain text, with data items separated by commas. In each type of data, a checksum is included for the sentences. These begin with \"*\" and two HEX digits representing the XOR action of all characters between the '$' (not including) and up to \"*\". A test amount is not required for all the sentences.  
In autonomous vehicles and vehicles in general, NMEA sentences are used in the following formats: GPGGA, GPGSV, GPGSA, GPRMC [30].
## 4 Conclusions  
Through laboratory experiments, DLVO theory, contact-efficiency calculations, and a physical clogging model, we obtained the critical threshold for preventing SE-induced clogging during AGR. Our findings revealed that SE with a turbidity of 1.84 \(\pm\) 0.060 NTU caused serious clogging, as did diluted SE with turbidities of 0.540 \(\pm\) 0.050 and 1.09 \(\pm\) 0.050 NTU. However, filtered SE with a turbidity of 0.160 NTU did not cause clogging. As suspended particles are transported, they likely collide with the sand matrix and are retained. Therefore, the turbidity threshold to prevent clogging should not exceed 0.500 NTU. Overall, this study provides a basis for setting water turbidity thresholds to limit physical clogging during AGR when using WWTP-derived SE. However, the nature of the aquifer in a recharge project is complex, usually mixed with various types of sand and soil. The clogging risk of simulated aquifers containing different particle-size distributions, types, and packing densities still needs further study.  
The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/w15030594/s1](https://www.mdpi.com/article/10.3390/w15030594/s1)]([https://www.mdpi.com/article/10.3390/w15030594/s1](https://www.mdpi.com/article/10.3390/w15030594/s1)), Figure S1: Linear calibration curves of NTU and SS for suspended particles; Figure S2: The pictures of the surface of porous medium at the end of the experiments; Figure S3: Clogging of infiltration safety threshold recharge water at a large-scale site; Table S1: Threshold values of suspended solid concentration and turbidity for reclaimed-water-quality standards in different regions; Table S2: The main water-quality parameters of secondary wastewater; Text S1: Formulae for \(\Phi_{\text{VDW}}\) and \(\Phi_{\text{EDL}}\).  
Conceptualization, W.F. and D.X.; methodology, S.L. and Y.W.; validation, S.L., S.W. and S.Z.; writing--original draft, S.L.; writing--review and editing, W.F. and D.X. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Natural Science Foundation of China (no. 51978135 and 52270089). It was also supported by the Scientific and Technological Development Plan Project of Changchun City (no. 21 ZY30) and Jilin Province (no. 20200201042 JC).
# Effect of Water Content and Cementation on the Shear Characteristics of Remolded Fault Gouge
## 2 Methods and Materials  
### Field Investigation and Component Analysis of Natural Fault Gouge  
To provide a scientific basis for the laboratory preparation of remolded fault gouge, field investigations are conducted in elastic rock fault zones within the Liujiang Basin, located in North China. During the investigation, both fault gouge and the rocks from the hanging wall and footwall are sampled. X-ray diffraction (XRD) analysis is performed on these samples to determine their mineralogical compositions.  
Field observations reveal that fault gouge occurs in both loose and cemented forms (see Figure 1). Notably, localized silicate cementation is observed in parts of the fault core, often filling the voids between the hanging wall and footwall. These cementation features are closely related to the saturation state of chemical fluids, the mineralogical composition of surrounding rocks, and ambient temperature-pressure conditions. However, consistent with previous studies [35; 36; 37], cementation is generally limited in scale and discontinuous in spatial distribution.  
The XRD analysis results (Figure 2) indicate that the mineral composition of fault gouge is similar to that of adjacent mudstone. On average, non-clay minerals, primarily quartz, account for 51.86%; expansive clay minerals, mainly montmorillonite, for 18.11%; non-expansive clay minerals, predominantly kaolinite, for 28.91%; and other components for 1.11%. These results serve as the foundation for selecting and proportioning minerals in the laboratory preparation of remolded fault gouge samples. In addition to mineralogical composition, we refer to the classification framework of fault-related rocks proposed by [PERSON] [38]. He distinguished between cohesive fault rocks (e.g., cataclasites andylonites) and incohesive fault materials such as clay-rich fault gouge. Based on field observations and sample texture, the collected fault gouge in this study shows weak cohesion and a very fine grain size, consistent with the characteristics of clay gouge, which typically forms in the shallow, brittle parts of fault zones. This supports the rationale for selecting clay minerals as the main constituents in our remolded experimental material.  
Figure 1: Fault gouge in clastic rocks and sampling locations: (**a**) infill type; (**b**) partially cemented type.
## 3 Results and Discussion
### Fungal Species in the Investigated Areas
[PERSON] and [PERSON] [68] confirmed the ability of _Pluteus chrysophaeus_, representing the same genus, to decompose phenanthrene, anthracene, and pyrene. Mycorrhizal fungi are also interesting for use in bioremediation, especially Boletaceae, which is a family of fungi with the potential to degrade POPs, including PAHs. Existing studies also show that the ectomycorrhizal species _Boletus edulis_ can degrade DDT [23]. In this study, the Boletaceae family was represented by several species representing the _Xerocomellus_ genus for which potential mycoremediation ability has not yet been discovered, giving a space for further investigation. Figure 3 summarizes the glomalin (GRSP) contents in soils, documenting the wide value range for both EEG and TG contents. These findings are expected because of variable soil properties at the individual sampling points and are promising regarding the potential degradation of organic pollutants. For instance, [PERSON] et al. [69] investigated the role of GRSP in the microbial degradation of the PAH residues tightly bound in the soil matrix. Figure 3: The contents of the easily extractable glomalin (**A**) and total glomalin (**B**) in soils according to the individual sampling sites. The results showed limited degradation of the bound PAHs by the microbial consortium used (not exceeding 15%), but high concentrations of GRSP (500, 1000 mg/kg) enhanced the degradation process. The importance of AMF symbiosis and, in this context, GRSP in the remediation of soils polluted by POPs was reviewed by [PERSON] et al. [70]. Although the enhanced sorption of PAHs on clay minerals in the presence of GRSP was already observed [71], the opposite pattern leading to an increase in PAH mobility in soils in the presence of GRSP was observed by other authors [72]. The proposed mechanism of enhancing PAH mobility is related to the dissolution of soil organic matter and increasing dissolved organic matter in the soil solution. [PERSON] et al. [32] observed lower GRSP contents in the soil highly polluted by crude oil but generally higher biological activity and higher diversity of PAH-degrading fungal species compared to the soils far from the most polluted place. These statements were not confirmed by this study because the full fungal community was not completely analyzed, and only the fruiting bodies occurring in the sampling areas were collected. However, we can speculate on the importance of the GRSP in the degradation of PAHs, especially in the case of long-term pollution characterized by the low bioaccessibility of the individual compounds.
## 3 Collective-Rotation Noise Analysis
### Numerical Results  
In this section, we present the numerical results of the three-stage protocol under the collective-rotation noise model. The error rate of a single photon and the error rate of multiple photons are plotted in Figure 3. We only plot the results for odd number photons as we have proved that the corresponding even number of photons with the same value of \(k\) produce the same error rate. From the figure we can see that the mean error rate increases as the deflection angle increases. In a multi-photon system, when the photons are influenced under the same deflection angle, increasing the number of photons used in transmission will decrease the mean error rate.  
The achievable bit error rate is important to a quantum cryptography system. From Figure 3, we can see that if the target bit error rate is 0.1, it is not achievable under \(\theta\) = 15\({}^{\circ}\) collective-rotation noise, even with 15 photons. If the target bit error rate is 0.2, under \(\theta\) = 15\({}^{\circ}\) collective-rotation noise, it is achievable with 15 photons. If a single photon is used, the error rate will get close to 0.4 when the deflection angle is 15\({}^{\circ}\). Increasing from 1 photon to 3 photons, the system can much better cope with collective-rotation noise. Likewise, under \(\theta\) = 10\({}^{\circ}\) collective-rotation noise, the error rate decreases from 0.2 to 0.1.  
The error rate _versus_ the numbers of photons with different deflection angles is plotted in Figures 4-6. By increasing the number of photons, the error rate decreases rapidly and gets close to 0 at some point. Under collective-rotation noise \(\theta\) = 5\({}^{\circ}\), the bit error rate gets close to 0 at 7 photons. Under \(\theta\) = 10\({}^{\circ}\), the bit error rate is close to 0 at 15 photons. When collective-rotation noise \(\theta\) = 15\({}^{\circ}\), it might be too noisy for the quantum cryptography system. Even with 15 photons, it has an error rate close to 0.2.  
Figure 4: Error rate _versus_ number of photons when deflection angle is 5\({}^{\circ}\).  
Figure 5: Error rate _versus_ number of photons when deflection angle is 10\({}^{\circ}\).  
By coupling collective noise information with the number of photons used in transmission, the system can be tuned to an operating point where [PERSON]'s siphoning of photons can be detected by the change of the bit error rate from the expected values. For example, under collective-rotation noise \(\theta\!=\!10^{\circ}\), if seven photons are used to transmit the information, the expected bit error rate is 0.03. If three to four photons get lost ([PERSON] needs to steal at least three photons to obtain information), the bit error rate will increase dramatically to 0.1, which is a significant change for the system. This will allow [PERSON] and [PERSON] to abort the transmission. At this operating point, the system is very sensitive to the loss of photons, therefore, can be used to detect [PERSON]'s disturbance of the system.
## 3 Description of the Clear Sky Models  
In the Adnot-Bourges-Campana-Gicquel (ABCG) model [34], the sun's zenith angle (\(\theta_{z}\)) (for different methods for calculation, see [76; 77; 78] for examples) is the main input that is required to be able to estimate clear sky GHI. The transmittance of 951.39 is already calibrated. The attenuation of extraterrestrial normal incoming irradiance (\(DNI_{TOA}\)) as it passes through the atmosphere is affected by the \(\theta_{z}\). The [PERSON], [PERSON] and [PERSON], Simplified Solis, [PERSON], Berger-Duffie, and [PERSON] clear models were elucidated in the study by [PERSON] et al. [45]. The SOLCAST satellite-based dataset was also elucidated in [39; 40; 42]. The basic models' equations are given by Equations (1)-(7).  
[PERSON] [34]:  
\[GHI=951.39\cdot\cos\theta_{Z}\text{.}^{1.15} \tag{1}\][PERSON] [13]:  
\[GHI=\frac{\left(DNI\ cos\theta_{z}+DIF\right)}{\left(1-R_{g}R_{s}\right)} \tag{2}\]  
[PERSON] and [PERSON] [32]:  
\[GHI=0.84 DNI_{TOA}\cos\theta_{z}e^{\left(-0.027 AM\left(f_{h1}+f_{h2}\left(T_{L}- 1\right)\right)\right)} \tag{3}\]  
Simplified Solis [30]:  
\[GHI=DNI^{\prime}_{TOA}e^{\left(\frac{-\tau_{g}}{\cos\theta_{z}^{2}}\right)} \cdot\cos\theta_{z} \tag{4}\]  
In Equation (4), to account for varying altitudes and atmospheric conditions, the \(DNI_{TOA}\) was changed to the enhanced direct normal extraterrestrial solar radiation \(DNI^{\prime}_{TOA}\), given by the equation below from [PERSON] [30]:  
\[\begin{array}{l}DNI^{\prime}_{TOA}=DNI_{TOA}\left(0.12P_{w}^{0.56}\cdot AOD \mathcal{D}_{700 nm}^{2}+0.97P_{w}^{0.032}\cdot AOD\mathcal{D}_{700 nm}\right.\\ \left.+1.08P_{w}^{0.0051}+0.017\ln\left(\frac{p}{1013}\right)\right)\end{array} \tag{5}\]  
[PERSON] [33]:  
\[GHI=DNI_{TOA}0.7\cos\theta_{Z} \tag{6}\]  
[PERSON] [35]:  
\[GHI=1098\ \cos\theta_{Z}e^{\frac{-0.057}{\cos\theta_{Z}^{2}}} \tag{7}\]  
The variables in Equations (1)-(7), can be defined as follows: DNI is the direct normal extraterrestrial irradiance; DIF is the diffuse horizontal irradiance; \(R_{g}\) is the ground albedo; \(R_{s}\) is the sky albedo; \(DNI_{TOA}\) is the direct normal extraterrestrial irradiance; \(AM\) is the relative air mass; \(f_{h1}\) and \(f_{h2}\) are the coefficients that relate the site altitude with the altitude of the atmospheric interactions; \(TL\) is the atmospheric transmissivity; \(\tau\) is the total atmospheric optical depth; \(b\) is the constant of adjustment for DNI; \(P_{w}\) is the precipitable water or water vapour in centimetres and \( AOD_{nm}\) is the aerosol optical depth at 700 nanometres and \(p\) is the surface pressure.