esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Network Model
### Packet Transport Network  
The transport of data flows between RUs, DUs, and CUs, as well as between CUs and the DC, is achieved by means of a packet-switched network. The packet transport network implements the TSN features defined in [8] with the aim to support the transport of latency-sensitive data. Three classes of traffic of different priorities, namely high priority (HP), medium priority (MP), and low priority (LP), are supported in the network. Fronthaul flows need the lowest latencies and they are served as the HP class. The MP class is assigned to midhaul flaws, which may tolerate higher latencies. Eventually, the backhaul traffic is served with the lowest priority.  
Each class of traffic has a dedicated queue at the switch output port. The selection of packets for transmission is performed based on the priority levels of packets, in accordance to the strict priority algorithm defined in [31]. In particular, a packet from a non-empty queue of the highest priority is selected first. For the queued up packets of flows of same priority, the selection may be arbitrary. Moreover, preemption of frames is not allowed in the switches [8]. Therefore, the transmission of a lower-priority packet must be completed before the transmission of a higher-priority packet is allowed.
# Effect of Sowing Dates on Fatty Acids and Phytosterols Patterns of _Carthamus tinctorius_ L.
## 4 Discussion
### Effect of Delaying Sowing Date on Fatty Acids Composition in Safflower Seeds  
The fatty acid composition of safflower seeds observed in our study was quite similar to those reported in several studies [13; 14]. High proportions of polyunsaturated fatty acids and sterols were observed, which confirm the potential interest of safflower oil for medicinal and dietetic purposes [15; 16; 17; 18; 19]. Unsaturated fatty acids represented 87.9% and 79.8% in the conventional and late sowing dates tested, respectively (Figure 3). Genotypic variation of fatty acid composition in safflower has been reported in a similar range [6; 16; 17; 18]. Small quantity of Arachidic (C20:0), behenic (C22:0) and ecosenoic (C20:1) acids was noticed (Figure 3) which is in accordance with results already observed in other reports on safflower [6; 20; 21; 22].  
Delaying the sowing date induced a decrease of unsaturated fatty acids in our experiment. Environmental effects (temperature or water regimes) have already been described to impact significantly the seed composition of several oil crops [23; 24; 25]. More recently, the effect of intercepted solar radiation was reported. This later study emphasized the importance of environmental conditions in determining oil and sterols quality in sunflower [26]. Nevertheless, these studies were performed around the world and reported differences in fatty acid content on collections of genotypes from several regions with different degrees of adaptation to environmental conditions. Interestingly, the increase of polyunsaturated fatty acids content under conventional sowing date was mainly due to the rise of linoleic acid content (Figure 3). These results are in contrast with those obtained in sunflower. Indeed, a negative correlation has been demonstrated between the two main unsaturated fatty acids (oleic and linoleic acids) under a late sowing date [24]. Those results may suggest specific pathways to accumulate unsaturated fatty acids in safflower when compared to sunflower. Moreover, on both species, it has been demonstrated that delaying sowing dates, which leads to higher temperatures, would be responsible for this negative correlation [14; 27]. The absence of correlation observed between oleic and linoleic acids seems more related to the water deficit during the experiment than to the difference in temperature between the two sowing dates (Figure 1 and Table 1). This result may be interesting in order to modify the potential composition of fatty acids for specific industrial purposes.
## 3 Experiment
### Model Evaluation Indicators
#### 3.3.1 Regression Task Evaluation Metrics  
The mean absolute error (MAE) measures the average magnitude of prediction errors without being overly sensitive to outliers. It is particularly suitable for scenarios where frequent fluctuations or anomalies are present in IDC environments. The formula is defined as follows:  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}|y_{i}-\hat{y}_{i}| \tag{20}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Name** & **CPU** & **GPU** & **CUDA** & **Operating System** & **Deep Learning Framework** \\ \hline \multirow{3}{*}{Configuration} & Intel i5-13600 KF & NVIDIA RTX 4060 & \multirow{3}{*}{12.6} & \multirow{3}{*}{Windows 11} & \multirow{3}{*}{PyTorch 2.6} \\  & (Intel Corporation, & (NVIDIA Corporation, & Santa Clara, CA, USA) & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental environment: software and hardware configuration.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **Layers** & \
\begin{tabular}{c} **Hidden** \\ **Dim /** \\ **d\_model** \\ \end{tabular} & **Dropout** &
\begin{tabular}{c} **Attention** \\ **Heads** \\ \end{tabular} & **Epochs** & **LR** \\ \hline \multirow{3}{*}{BiCA-LI (full)} & LSTM: 2, & LSTM: 64, & \multirow{3}{*}{0.1} & \multirow{3}{*}{4} & \multirow{3}{*}{50} & \multirow{3}{*}{\(1\times 10^{-3}\)} \\  & Informer: & Informer: & & & & \\ \cline{1-1}  & 3 & 128 & & & & \\ \cline{1-1} LSTM-only & 2 & 64 & 0.1 & - & 50 & \(1\times 10^{-3}\) \\ \cline{1-1} Informer-only & 3 & 128 & 0.1 & 4 & 50 & \(1\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model-specific architecture and training hyperparameters.  
where \(n\) denotes the number of samples, \(y_{i}\) represents the true value of the \(i\)-th sample, and \(\hat{y}_{i}\) is the corresponding predicted value.  
The mean squared error (MSE) emphasizes larger deviations by squaring the residuals, making it more sensitive to large prediction errors. This metric is useful for detecting sharp changes in IDC equipment behavior. Its calculation is as follows:  
\[\text{MSE}=\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2} \tag{21}\]  
The root mean squared error (RMSE) provides an intuitive measure of the average magnitude of prediction errors in the same unit as the target variable. It is especially useful for comparing model performance against real-world physical quantities. The RMSE is calculated as  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}} \tag{22}\]
# Mapping Smallholder Yield Heterogeneity at Multiple Scales in Eastern Africa
## 3 Results
### Performance of the Calibrated Model  
The preferred calibrated model for the B-K sub-region explained 28.2% and 35.6% of yield variations in purestand fields when using GCVI and MTCI as explanatory variables, respectively (Table 3). Such performances were competitive to the model proposed by [7] for the similar region, which purely used cloud-free Skysat imagery as explanatory variables. The remaining unexplained variation in yields can be attributed to other factors such as the presence of weeds and the fraction of the planted area that was not harvested (typically due to drought, waterlogging, animal damage, or theft). However, these factors were not quantitatively surveyed and thus could not be assessed directly.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Vegetation**} & \multicolumn{2}{c}{**B-K Sub-region**} & \multicolumn{2}{c}{**1 AF Region**} \\ \cline{2-5}  & **Full** & **Purestand** & **Division** & **District** \\ \hline GCVI & 0.18 & 0.28 & 0.29 (0.36) & 0.43 (0.49) \\ MTCI & 0.20 & 0.36 & 0.36 (0.44) & 0.57 (0.66) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Adjusted coefficient of determination (R\({}^{2}\)) of calibrated regression model that was trained using ground yield data. Values for the B-K sub-region (Bungoma and Kakamega counties) were derived from regressions with fields larger than 0.5 acre. Values in the brackets were derived from regression that excluded two counties with early sowing. Values within each column or each row were not statistically different based on F-tests. 1 AF = One Acre Fund.  
The calibrated model for the 1 AF Region that used Tavg and the coarser resolution Sentinel-2 imagery as the explanatory variables had fairly good performance, especially at the district level where aggregated yield measurements were most reliable (Table 3; Figure 5a,b). Using MTCI instead of GCVI improved the model adjusted R\({}^{2}\) by 25% at the division level and 31% at the district level, although MTCI data were only half of the spatial resolution as that of the GCVI data due to the coarser resolution of red edge bands of Sentinel-2 (Table 1). Excluding districts from the \"Kisii\" and \"Nyamira\" County in the south that had either much earlier or wider range of sowing dates further improved calibrated models (Table 3). Yield estimates were mostly between 2 to 4 t/ha and were slightly smaller than the measurement range of 1 to 4 t/ha (Figure 5a,b). The best performance was achieved when using MTCI at the district level, which captured 57% and 66% of the crop cut yield variations with and without the two early sowing counties, respectively. This level of agreement between predicted and measured yields was close to [23], who trained a multiple regression model for southwestern Kenya with a range of satellite and ground-level data such as NDVI, agrometeorological data, and government produced maps of crop yields and area planted.
## 2 Materials and Methods
### Parameters of Index Insurance  
With a similar approach to Hill and Robles [53] and the Nyala Insurance Share Company [54], we established the PTPM as the index variable. To define the parameters, we found the average monthly water requirements for Arabica crops. These requirements rely on different factors including altitude, the quality and humidity of the soil, and the age of the crop [55]. Usually, this type of information is hard to acquire; thus, we used the values recommended by the FNCC (National Federation of Coffee Growers of Colombia), which are based on altitude ranges. In summary, the FNCC establishes optimal average monthly waterings of 57 and 135 PTPM. The upper and lower limits were set following the same procedure established in this study by [PERSON] et al. [18]. The lower limit was established as 40 PTPM and the upper limit as 241 PTPM [55].  
The amount insured was calculated by using a similar methodology as used by [PERSON] et al. [18] and by [PERSON] [56]. This amount was based on a currency exchange rate of 3478 COP-USD [57] and a reported coffee market price of USD 2427/ton (or 1,055,000 COP) per coffee bag. In the main and Mitaca crop production season, which yields40% of the first crop season, 30 bags of dried coffee are added annually. For growers to reach maximum indemnity, there must be a maximum production loss of up to 75% caused by extreme weather. Therefore, the maximum indemnity is USD 761 (\(2.69\times 10^{6}\) COP/ha), as indemnity is 1.16 USD/kg [58]. In summary, the lower and upper limits are set to 40 and 241 PTPM, respectively. Similarly, the lower and upper thresholds are 57 and 135 PTPM. The sum insured is set at 15 M COP per ha. The maximum indemnity (MI) is 2.7 M COP per ha.  
The referenced payout function follows a linear index insurance model [19]. The marginal payouts for low PTPM levels are derived linearly by dividing the maximum payoff or indemnity of 2.7 M COP/ha per one PTPM, by the difference of 40 PTPM (lower limit). The threshold for drought is 57 PTPM (about 2.24 in) (Figure 5a). Similar considerations for the case of high rain levels can be seen in Figure 5b.  
We previously derived a simulated kernel distribution of PTPM with \(n=1000\) points from the year time series data for each of the weather stations (2010-2019) to calculate the payments linear function (POs) (3) for wet and dry crop seasons. The pricing linear function follows both PTPM conditions, \(m_{0}\) (2) and \(m_{1}\) (3), applied to the first component and season \(i\), where \(i\in[1,2]\).  
\[PO(PTPM)_{i}=\left\{\begin{array}{c}M.I.\;if\;PTPM<LL\;or\; PTPM\geq UL\\ \;\;\;\;\;\;\;\;\
# End-of-Life Impact on the Cradle-to-Grave LCA of Light-Duty Commercial Vehicles in Europe
###### Abstract  
A cradle-to-grave life cycle assessment focused on end-of-life (EoL) was conducted in this study for three configurations of a light-duty commercial vehicle (LDCV): diesel, compressed natural gas (CNG), and battery electric vehicle (BEV). The aim is to investigate the impact of recycling under two EoL scenarios with different allocation methods. The first is based on the traditional avoided burden method, while the second is based on the circular footprint formula (CFF) developed by the European Commission. For each configuration, a detailed multilevel waste management scheme was developed in compliance with the 2000/53/CE directive and ISO22628 standard. The results showed that the global warming potential (GWP) impact under the CFF method is significantly greater when compared to the avoided burden method because of the A-parameter, which allocates the burdens and benefits between the two connected product systems. Furthermore, in all configurations and scenarios, the benefits due to the avoided production of virgin materials compensate for the recycling burdens within GWP impact. The main drivers of GWP reduction are steel recycling for all of the considered LDCVs, platinum, palladium, and rhodium recycling for the diesel and CNG configurations, and Li-ion battery recycling for the BEV configuration. Finally, the EoL stage significantly reduces the environmental impact of those categories other than GWP.  
waste management treatment of vehicles; end-of-life (EoL) of vehicles; life cycle assessment (LCA); light duty commercial vehicles; avoided burden approach; circular footprint formula; ELVS
## Background
### Protocol for Proving the Linearity of Discrete Logarithms  
We review the generalized interactive protocol for proving the linearity of discrete logarithms. Let \(\mathbb{G}_{1},\ldots,\mathbb{G}_{n+1}\) be groups of prime order \(p\), and let \(g_{i}\in\mathbb{G}_{i}\) be a generator for \(i\in[n+1]\). Now, we consider the NP-language  
\[\mathcal{L}=\{(g_{1},\ldots,g_{n},g_{n+1},A_{1},\ldots,A_{n},A_{n+1}):\] \[\exists\ (x_{1},\ldots,x_{n})\in\mathbb{Z}_{p}^{n}\text{s.t.}\ A_{i}=g _{i}^{x_{i}}\ \text{for}\ i\in[n],\ A_{n+1}=g_{n+1}^{x_{1}+\cdots+x_{n}}\}.\]  
The discrete logarithm \(\text{wt}=(x_{1},\ldots,x_{n})\) is a witness for a statement \(\text{stmt}=(g_{1},\ldots,\)\(g_{n},g_{n+1},\)\(A_{1},\ldots,A_{n},A_{n+1})\). Then, the interactive protocol \((\mathcal{P},\mathcal{V})\) for proving the above language is as follows.  
1. For a statement \(\text{stmt}=(g_{1},\ldots,g_{n},g_{n+1},A_{1},\ldots,A_{n},\)\(A_{n+1})\), the prover \(\mathcal{P}\) selects random exponents \((r_{1},\ldots,r_{n})\in\mathbb{Z}_{p}^{n}\) and computes a commitment \(\text{com}\) as follows. \[\text{com}=(B_{1},\ldots,B_{n},B_{n+1})=(g_{1}^{r_{1}},\ldots,g_{n}^{r_{n}},g_ {n+1}^{r_{1}+\cdots+r_{n}}).\]
2. The verifier \(\mathcal{V}\) randomly selects a random challenge \(\text{chal}=c\in\mathbb{Z}_{p}\).
3. \(\mathcal{P}\) computes a response \(\text{resp}=(s_{1},\ldots,s_{n})\), where \(s_{i}=r_{i}+cx_{i}\in\mathbb{Z}_{p}\) for \(i\in[n]\).
4. Given a proof \(\pi=(\text{com},\text{chal},\text{resp})\), \(\mathcal{V}\) checks that \[B_{1}=g_{1}^{s_{1}}A_{1}{}^{-c},\ldots,B_{n}=g_{n}^{s_{n}}A_{n}{}^{-c},\ \text{and}\ B_{n+1}=g_{n+1}^{s_{1}+\cdots+s_{n}}A_{n+1}{}^{-c}.\]  
Similarly to the equality of discrete logarithms, this protocol is also a non-trivial three-round public coin interactive proof system satisfying completeness, HVZK, (statistical) soundness, and _unique response_ properties.
# Evaluation of Multi-Source High-Resolution Remote Sensing Image Fusion in Aquaculture Areas
## 2 Materials and Methods
### Image Data  
Three types of high-resolution remote sensing data were used in this study to conduct a series of fusion evaluation experiments in aquaculture areas. Descriptions of these data types are listed in Table 1.  
China launched six HDEOS (high-resolution Earth observation system) space crafts between 2013 and 2016 [21]. GF-1 is the first of a series of high-resolution optical Earth observation satellites of the CNSA (China National Space Administration), Beijing, China; GF-2 is a follow-up mission of GF-1 with the objective to provide high accuracy geographical mapping, land and resource surveying, environment change monitoring, and near-real-time observation for disaster prevention and mitigation, as well as agriculture and forest estimation [22]. As listed in Table 1, GF-1 and GF2 have the same spectral resolution but different spatial resolutions. The WV-2 sensor provides a high-resolution panchromatic band and eight (8) multispectral bands--four (4) standard colors (red, green, blue, and near-infrared 1) and four (4) new bands (coastal, yellow, red edge, and near-infrared 2)--and full-color images for enhanced spectral analysis, mapping and monitoring applications, land-use planning, disaster relief, exploration, defense and intelligence, and visualization and simulation environments [23].  
Figure 1: Location map of the study area.  
Figure 2: The scenes of different types of aquaculture areas. (**a**) Enclosure seines culture area; (**b**) floating rafts culture area.  
Figure 3 shows the flow chart of this study. PCA (principal component analysis), GS (Gram-Schmidt), and NNDiffuse (nearest neighbor diffusion) algorithms using ENVI software were applied to PAN and MS images fusion of GF-1, GF-2, and WV-2 in the areas of seine culture and floating rafts. PCA is a statistical technique that transforms multivariate data with correlated variables into uncorrelated variables [24; 25]. The GS orthogonalization procedure is a powerful pan sharpening method [26]. NNDiffuse assumes that each pixel spectrum in the pan-sharpened image is a weighted linear mixture of the spectra of its immediate neighboring super pixels; it treats each spectrum as its smallest element of the operation, which is different from the most existing algorithms that process each band separately [27]. Two quantitative methods are used to evaluate the fusion effects. One uses the seven traditional statistics indexes, and the other compares the \(CQ_{max}\) index to find which image fusion method is the best for each type of satellite sensor and to answer the question of whether there is a universally good method for high spatial resolution images.
## 3 Methodology
### Proposed Framework
#### 3.1.4 Cloud Layer  
Data from each node in the edge layer, which were subsequently processed and controlled at the base station, were visualized in the cloud layer using an Adafruit IO Cloud platform. Farmers could track crop cultivation progress using a graphical interface (UI)-based application.
## 5 Results and Discussion
### Verification and Analysis of Theoretical Model
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Power/W** & **Scribing Speed** & **Laser Power** & **Measurement** & **Standard** & **Prediction Value** & **Prediction Accuracy** & **Prediction Value** & **Prediction Accuracy** \\  & **(mm/min)** & **Density(W/mm\({}^{2}\))** & **Average Value** & **Deviation** & **of Equation (13)** & **of Equation (13)** & **of Equation (14)** & **of Equation (14)** \\ \hline
0.68 & 13.333 & 0.34 & 80.314 & 2.915 & 70.6766 & 88.00\% & 71.762 & 89.39\% \\
2.67 & 40 & 0.445 & 107.766 & 2.857 & 89.5231 & 83.0\% & 91.633 & 85.03\% \\
1.315 & 17.15 & 0.511 & 129.357 & 2.164 & 101.369 & 78.36\% & 104.124 & 80.49\% \\
1.97 & 24 & 0.547 & 120.668 & 2.942 & 107.831 & 89.37\% & 110.937 & 91.94\% \\
4.915 & 30 & 1.092 & 209.695 & 2.287 & 205.653 & 98.0\% & 214.078 & 97.91\% \\
3.91 & 20 & 1.303 & 263.722 & 2.368 & 243.525 & 92.34\% & 254.010 & 96.32\% \\
3.29 & 15 & 1.462 & 300.700 & 3.051 & 272.064 & 90.48\% & 284.100 & 94.48\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Prediction results of testing set data. According to mathematical Equation (13), the minimum accuracy of the prediction equation is 78.36%, the maximum value is 98.07% and the average prediction accuracy is 88.53%. The accuracy of mathematical Equation (13) at different test points is shown in Figure 12(a). According to mathematical Equation (14), the minimum accuracy of the prediction equation is 80.49%, the maximum value is 97.91% and the average prediction accuracy is 90.79%. The accuracy of mathematical Equation (13) at different test points is shown in Figure 12(b).
# Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5
## 3 Experiments
### Experimental Analysis  
After training our model, we made predictions for the new and unseen pictures in our test set. The examples in Figure 6 show that the algorithm can detect the front of a truck to a higher degree of certainty. However, it has difficulty recognising the rear of a truck, especially when these are located far away from the camera. It also detects a car as a _truck_front_ in two of the images.  
It can be seen that the algorithm currently struggles to correctly differentiate between cars and cabins, and this becomes worse the more truck fronts are present in an image. It is also difficult for the algorithm to correctly recognise truck rears in an image. Strategies to overcome these shortcomings are proposed in Section 4.  
To evaluate the model trained with the rest area dataset, we compared it to YOLOv5 [50], without it being trained on any additionally data as a baseline model only using COCO weights. This model contains, amongst other classes, the _car_ and _truck_ class, however, it does not distinguish between _truck_front_ and _truck_back_. Table 2 shows the accuracy of the baseline and the altered model for the four available classes.  
Figure 5: Plots of box loss, objectness loss, classification loss, precision, recall and mean average precision (mAP) over the training epochs for the training and validation set.  
\begin{table}
\begin{tabular}{l l c} \hline \hline
**Class** & **YOLOv5** & **YOLOv5 + Rest Area Data** \\ \hline truck & 0.42 & - \\ truck\_front & - & 0.63 \\ truck\_back & - & 0.52 \\ car & 0.78 & 0.93 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracy of the baseline model, You Only Look Once (YOLO)v5, for the classes _truck_ and _car_, and the altered model, YOLOv5, trained with additional data from the rest area dataset for the classes _truck_front_; _truck_back_; and _car_.  
Figure 6: Images form the test dataset showing the performance for detecting the three classes _truck_front_; _truck_back_; and _car_.  
The baseline model, which is trained on heavy goods vehicles as a whole, had difficulties detecting them on the test images of the rest of the area's dataset. It either did not recognise the trucks or it did so with much less certainty than the altered model with the two new classes. The additional training also improved detecting heavy goods vehicles on images on which the cabin was cut off. Some examples of detection for the test data of the two models are shown in Figure 7.