esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### FTIR-ATR Measurements  
Fourier-transform infrared (FT-IR) spectra were recorded at 25 \({}^{\circ}\)C in attenuated total reflectance (ATR) mode by using a single reflection horizontal ATR accessory with a diamond crystal fixed at an incidence angle of 45\({}^{\circ}\) (Platinum ATR, Bruker). The crystal was mounted on a Vertex 80V FT-IR spectrometer (Bruker). The investigated spectral range was 30-300 cm\({}^{-1}\). A background scan was recorded prior to the measurement and was subtracted from the sample spectra. Each spectrum was averaged over 216 scans with a resolution of 2 cm\({}^{-1}\) and corrected ATR. Measurements were carried out in an evacuated optics bench configuration in order to eliminate atmospheric moisture effects. The acquired far-IR spectra was decomposed into Gaussian components by means of standard algorithms with free parameters (95% confidence interval). The optimal number of components was determined through a careful statistical study following the procedure described in [6].
## 6 Requirements for Using IoT in SG  
To use IoT in SG, we should have some technologies and satisfy some requirements which are listed as follows:  
1. Communication technologies: Communication technologies can be used to receive and transmit acquired information about the state of SG's devices. We have short-range and long-range communication technology standards. ZigBee, Bluetooth, and ultra-wideband technologies are examples of short-range communication technologies. For long-range communications, power line communications [54], optical fiber, wireless cellular networks such as 3G and 4G, and satellite communications can be used.  
2. Data fusion techniques: Since the resources of IoT terminals (such as batteries, memory, and bandwidth) are limited, it is not possible to send all information to the destination. Thus, to increase the efficiency of information gathering, data fusion techniques can be utilized to collect and combine data.  
3. Energy harvesting process: Since most of the IoT devices use battery as one of their primary power sources, energy harvesting process is very important for IoT applications, e.g., using different sensors and cameras to monitor different parts of a smart grid.  
Operating in harsh environments: IoT devices which are installed in high-voltage transmission lines and substations must work in harsh environments. Thus, to extend the lifetime of their sensors in these conditions, we should have sensors should be high or low temperature resistant, anti-electromagnetic, or waterproof.  
Reliability: IoT applications in different environments need to satisfy different requirements such as reliability, self-organization, or self-healing. Thus, based on the actual environment, suitable IoT device must be selected to overwhelm environmental issues. For example, when some devices cannot send data due to lack of energy, a new route for the data must be found so that the network reliability remains at the required level.  
Security: Security methods must be implemented in all IoT layers to transmit, store, and manage data, avoid information leakage and losses, and protect data.  
Sensors: Sensors measure quantities such as current, voltage, frequency, temperature, power, light, and other signals and deliver the raw information for processing, transmitting, and analyzing. Recently, nanotechnology is used to provide high-performance material which covers different sensor applications and enhances the growth of sensor industry.
## 4 Discussion
However, in comparison with Soxhlet extraction, higher results were obtained with thermal agitator extraction method, followed by ultrasound-assisted extraction and infusion (6.18 \(\pm\) 0.82 mg CC/g; 3.18 \(\pm\) 2.05 mg CC/g; 2.44 \(\pm\) 0.09 mg CC/g). _Eucalyptus_ bark extraction with ultrasound method also obtained remarkable results (2.64 \(\pm\) 0.12 mg CC/g). Lower anthocyanins content was found in _Tradescantia fluminensis_, from Soxhlet extraction (0.13 \(\pm\) 0.05 mg CC/g) compared to ultrasound extraction (0.27 \(\pm\) 0.13 mg CC/g). _Eucalyptus globulus_ leaves demonstrate higher phenols concentration; thermal agitator obtained the highest results (172.40 \(\pm\) 44.53 mg GAE/g), followed by ultrasound-assisted extraction and infusion extraction (94.27 \(\pm\) 55.83 mg GAE/g; 57.39 \(\pm\) 24.38 mg GAE/g), and with difference, Soxhlet extraction obtained the lowest results for _Eucalyptus_ leaves (29.76 \(\pm\) 1.86 mg GAE/g). In comparison with another study [4], where phenolic compounds with Soxhlet extraction using water as solvent for _Eucalyptus globulus_ leaves were 7.68 \(\pm\) 0.41 mg GAE/g, the developed experiments show better higher quantities. In _Eucalyptus globulus_ bark, results were also better for thermal agitator, followed by ultrasound extraction, Soxhlet extraction, and with lowest results, infusion. Comparing obtained content for _Tradescantia fluminensis_ with those obtained by study [3], for thermal agitator extraction at 45 deg in 6 h, (4.21 \(\pm\) 1.65 mg GAE/g leaves and 1.96 \(\pm\) 0.35 mg GAE/g stems) and [3] (1.90 \(\pm\) 0.57 mg GAE/g leaves and 1.15 \(\pm\) 0.31 mg GAE/g stems) results show that higher times and lower temperatures, and in-distinction of leaves and stems obtained higher phenols concentration (11.75 \(\pm\) 0.60 mg GAE/g leaves and stems at 40 deg in 8 h). In relation with _Arundo donax_ extraction, obtained results are higher than expected compared with study [2] (1.60 \(\pm\) 0.22 mg GAE/g leaves 0.93 \(\pm\) 0.42 mg GAE/g stems). Obtaining higher content with thermal agitator extraction (26.31 \(\pm\) 0.00 mg GAE/g) followed by ultrasound extraction, Soxlet, and infusion extraction (13.98 \(\pm\) 0.00 mg GAE/g; 9.11 \(\pm\) 0.00 mg GAE/g; 1.39 \(\pm\) 0.00 mg GAE/g). After analyzing all obtained results, it was demonstrated that there is a covariation between the extraction method and anthocyanins and phenolic content. Thermal agitator obtained the highest content in both anthocyanins and phenolic compounds. In contrast, Soxhlet and infusion extraction methods obtained the lowest concentrations, regardless of the plant organ. In relation to raw matter and contents, _Eucalyptus_ leaves have higher phenolic and anthocyanins content, and _Tradescantia fluminensis_ have the lowest anthocyanin and phenolic concentration.
# Clutter-Masked Waveform Design for LPI/LPD Radarcom Signal Encoding
## 5 Clutter-Masked Radarcom Waveform Performance Evaluation  
### Transmit Waveform Design  
Once the underlying clutter model has been ascertained, our goal becomes to create radarcom signals that would appear like clutter returns to an unauthorized receiver. We define the general format of OFDM sub-carrier coefficients \(S(m)\) as:  
\[S(m)=W(m)\cdot e^{\phi(m)}, \tag{11}\]  
where \(W(m)\) is the \(m^{\text{th}}\) sub-carrier magnitude, and \(\phi(m)\) is its phase. We then aim to assign samples drawn from the clutter random process with the modeled PDF to \(W(m)\), and samples drawn from a uniformly distributed random process to \(\phi(m)\).  
In order to accomplish the above, we first generate random samples from the uniform distribution within the interval \([0,1]\), and input them into the Arcsine distribution's inverse CDF (8), which then outputs random samples drawn from the Arcsine distribution. We used the uniform distribution with interval \([0,2\pi]\) to generate random phase samples \(\phi(m)\), which is characteristic for clutter returns [23].
## 3 Results
### Responses of the SIF-IWUE Relationship to the Environmental Factors
#### 3.3.1 Effects of VPD on Correlations between IWUE and SIF Emission  
VPD is typically used to represent water stress on plants [21]. Strong water stress may weaken the link between photosynthesis activities and SIF emission [30; 31]. Further, one important motivation to develop the different WUE formulations is to remove the nonlinear effect of VPD on \(T\). Thus, it is essential to assess whether the SIF-IWUE relationship is still affected by VPD. To do so, we calculated the \(R^{2}\) between SIF emission and IWUE at an hourly time step under three VPD levels: (1) low: VPD \(\leq\) 0.5 kPa, (2) medium: 0.5 kPa \(<\) VPD \(\leq\) 1.0 kPa, and (3) high: VPD \(>\) 1.0 kPa.  
The results from the LR models indicate that VPD has no clear negative impact on the SIF-IWUE correlations (Figure 3a). For example, SIF\({}_{761}\) has almost the same performance by explaining 25%, 17%, and 25% of the variance in IWUE under these three VPD levels, respectively (Figure 3a). Further, VPD also has no obvious influence on the performance of the SIF band combinations. The combination of SIF\({}_{687}\), SIF\({}_{720}\), and SIF\({}_{761}\) determines 44% and 41% of the variance in IWUE under the low and intermediate VPD levels and its performance only decreases slightly to 39% under high VPD values (Figure 3a). Again, the results from the GPR models show that the SIF-IWUE correlations have a relatively low sensitivity to VPD (Figure 3b). For instance, SIF\({}_{761}\) determines 39% of variability in IWUE when VPD values are high, and the counterparts decrease to 37% and 22% when VPD values are low and medium (Figure 3b). The predictive power of combined SIF bands for IWUE shows a moderate improvement under the high VPD level. The \(R^{2}\) of the combination of SIF\({}_{687}\), SIF\({}_{687}\), and SIF\({}_{761}\) shows a correlation of \(R^{2}\) = 0.69 against IWUE for the high VPD level, while it decreases to 0.62 and 0.53 under the low and intermediate VPD levels, respectively (Figure 3b).
# Evaluation of 6 LoWPAN Generic Header Compression in the Context of a RPL Network
## 4 Generic Header Compression
### Decomposition  
6 LoWPAN-GHC decompression is based on a dictionary and bytecodes. Bytecodes are byte-sized instructions for the decompressor. Before processing the compressed data, a dictionary is initialised with the source and destination IPv6 addresses of the packet. The dictionary is also appended with 16 bytes, for which the values are given in the standard (here shown as hexadecimal values): 16 fe fd 17 fe fd 00 01 00 00 00 00 01 00 00. The values 16 fe fd and 17 fe fd are used in DTLS handshake and application packets, respectively, with fe fd being the DTLS version. Furthermore, two variables _sa_ and _na_ are initialised to 0 and used for performing extended backreferencing. After the initialisation of the dictionary and variables, the decompressor starts reading the compressed bytes one by one. The compressed bytes contain bytecode instructions. These instructions can perform five different actions:  
1. Copy compressed bytes to the decompression buffer. The bytecode contains the number of bytes to copy to the decompression buffer, with a maximum of 96 bytes.
2. Append zeros to the decompression buffer, with a maximum of 17 zeros. The number of zeros is contained in the bytecode.
3. Set up _sa_ and _na_ to some specified values, contained in the bytecode. These variables are used for the backreferencing.
4. Perform backreferencing using the _sa_ and _na_ variables and extra information contained in the bytecode. The backreferencing is performed using the already decompressed bytes, prepended with the dictionary.
5. A stop instruction is only used when compressing IPv6 extension headers.
# Sillcom: A Communication-Efficient Privacy-Preserving Scheme for Indoor Localization
## 6 \(\text{Sillcom}_{trd}\): Customized Optimization with Computation/ Communication Tradeoff
### Optimized Top-\(k\) Circuit for Small \(k\)  
The design of a top-\(k\) circuit refers to efficiently organizing comparators to get the top-\(k\) results from \(m\) values. While methods such as quicksort can achieve sorting with low complexity and then extract the top-\(k\), such non-data-oblivious designs leak access patterns in secure computation. A naive data-oblivious circuit design employs a bubble sort approach, requiring \(O(mk)\) comparators but also introducing \(O(mk)\) rounds (depth for comparators). A straightforward improvement builds a binary tree structure for each top-\(i\) computation, reducing the number of rounds to \(O(k\log m)\). However, as mentioned earlier, secure computation requires keeping cryptographic costs in mind. In FSS-based secure comparisons, the computational cost per comparison is significantly lower than communication latency, so an optimized design should focus on a higher degree of parallelism in the top-\(k\) circuit. Sorting networks [54] are widely used in this direction; for example, [PERSON]'s odd-even sorting network [55] reduces the number of comparators to \(O(m\log m)\), but it still requires \(O((\log m)^{2})\) rounds.  
In indoor localization, where positioning can be achieved with a very small \(k\), we can optimize sorting networks by discarding the \(m^{\prime}-k\) non-top-\(k\) values within each small subset of size \(m^{\prime}\). Inspired by [56], we design the top-\(k\) circuit shown in Figure 6. First, \(m\) is partitioned into smaller subsets of size \(m^{\prime}\), where each small subset computes top-\(k\) using the design from \(\mathrm{Sillcom}_{3R}\), discarding the remaining \(m^{\prime}-k\) values. The resulting top-\(k\) values from multiple small sunsets are then merged into new sets to compute top-\(k\) iteratively until the top-\(k\) results over the entire \(m\) values are obtained. In the example shown in Figure 6, each small subset extracts the top-\(3\) from \(12\) values, achieving a fourfold data compression in each parallel top-\(3\) stage. We refer to such a circuit as top-\(k\) in a multi-branch tree manner.  
In practical applications, it is essential to select an appropriate subset size \(m^{\prime}\). A large \(m^{\prime}\) can reduce the number of rounds, but its \(O(mm^{\prime})\) computational complexity may offset the advantage gained from fewer rounds. Conversely, a small \(m^{\prime}\) may not achieve efficient circuit compression. Therefore, choosing \(m^{\prime}\) requires balancing network latency and computational capacity. In this paper, we adopt a two-layer multi-branch tree structure to address the feature of relatively small \(k\) in indoor localization scenarios. The effectiveness of this approach is verified through experimental results. As \(m\) extends larger, additional layers can be incorporated into the structure, further reducing computational overhead by increasing the number of communication rounds.  
Figure 6: Top-\(k\) in multi-branch tree.
# Pharmacokinetics of Marine-Derived Drugs
## 3 Pharmacokinetics Studies in Animals
### Metabolism  
#### 3.3.1 Algal-Derived Compounds  
For a better understanding of the possible sites for the metabolism of fucoidan (**1**), rats were fed a diet of fucoidan supplemented with \(N\)-butyl-\(N\)-(4-hydroxybutyl) nitrosamine (NN4 HN). NN4 HN intensifies the intestinal absorption of fucoidan [144]. The immunohistochemical assay revealed the presence of fucoidan in the small intestine, the epithelial cells of the jejunum, and the mononuclear cells of the lamella gland of the jejunum [145]. [PERSON] et al. reported the involvement of Kupffer cells (specialized liver macrophages) in the metabolic degradation of \(\beta\)-D-glucans [146]. As a ligand for macrophage scavenger receptor-A [147], fucoidan was accumulated by Kupffer cells [145].  
GRT was found in unchanged forms in the feces and urine of rats after i/v, s/c, and peroral administrations [51].  
Some sugars, such as D-glucose, are resorbed by renal tubules after filtration by the kidney [148]. As soon as alginate oligosaccharides (**2**) was not detected in the plasma 2 h after peroral administration, the authors suggested that it is not resorbed by the renal tubules [43].  
The metabolism of halomon (**3**) was studied in vitro in mice and human liver cells. The authors found that halomon is metabolized by mouse and human hepatic cytochrome P-450 enzymes, resulting in the loss of one chlorine atom and one bromine atom [75]. These data suggest the hepatic metabolism of the drug, consistent with the pharmacokinetic data previously obtained by the same group of authors [33].  
As a result of hydrolysis by digestive enzymes, lipase and cholesterol esterase, in the gastrointestinal tract, fucoxanthin (**5**) converts to fucoxanthinol (**23**) and is absorbed by the intestine [149]. Then, it is metabolized to amarouciaxanthin A in the liver by short-chain dehydrogenase/reductase [150]. Fucoxanthinis converted into fucoxanthinol by deacetylation [149; 151]. Fucoxanthin is quickly metabolized into fucoxanthinol after both i/v and peroral administration in rats as evidenced by LC-MS/MS analysis [42]. This evidences the important contribution of fucoxanthinol to the pharmacological activity of fucoxanthin.
# Insights into Segmentation Methods Applied to Remote Sensing SAR Images for Wet Snow Detection
## 2 Data and Method  
### Location and Time Period  
The area under investigation was part of the French Alps, as shown in Figure 1. This figure also shows the elevation and massif limits of the study area. The test zone was the 31 TGL tile (red frame in Figure 1), which fully covered seven massifs of the French Northern Alps and partially covered five other massifs. Our area was quite a heterogeneous alpine zone with complex topography and terrain and several land cover types, including forests, bare ground, glaciers and wetlands. The study period covered the 2017-2018 winter season, which was characterized by very high snow excess due to many disturbed passages [29; 30]. Very cold periods (26 and 27 February 2018) followed milder periods, which favoured the presence of wet snow and episodes of rain on snow. Finally, the spring of 2018 saw consistent melting periods from April, with a few scattered colder episodes.
## 3 Results and Discussion
### Image Processing  
Example angiography results and edge detection results are shown in Figures 15 and 16. With angiography and edge detection, the scattering angle indices can be categorized by vascular zone or retinal layer.  
Figure 14: Sample SAR-OCT images (**A**,**B**) show cross-sectional images of the same mouse at two different B-scan locations. Pathlengths 1–3 are shown. Each pathlengths has been averaged to obtain images (**C**,**D**). Scale bars indicate 300 \(\upmu\)m.  
Figure 15: Results of CDV angiography and additional filtering. Original CDV images (Row 1) are manually filtered to remove bulk motion artifacts due to breathing and heart beat (Row 2). A Frangi vesselness filter is then applied to improve SNR (Row 3). Lastly, the Frangi-filtered images are binarized using adaptive filtering (Row 4). Columns 1–3 show different vascular plexuses, and Column 4 shows composite images of the Frangi-filtered and binarized angiograms. Scale bars indicate 300 \(\upmu\)m.  
To obtain a \(\mathrm{L/H_{2}}\) tomogram, Pathlengths 1 and 2 are divided at every voxel position. With this approach, the segmentation output can be superimposed on the volume to sort the retinal \(\mathrm{L/H_{2}}\) values into their corresponding layers (Figure 17).
## 4 Proposed Dictionary Lookup Approach for Urdu Lemmatization  
This section discusses the proposed approach for Urdu lemmatization, based on the dictionary lookup approach. The proposed approach is dependent on the corpus and dictionary presented in Section 3.  
Algorithm 1 shows the proposed algorithm, called the Dictionary Look-up approach, the table look-up approach or the lexical look-up approach. In this proposed algorithm, the proposed dictionary contains the word, part of the speech tag, and the lemma (root) of a word. In step 1, take the input text for lemmatization. There are four variations in inputs: two inputs for the sequence of words (sequence of words with part of speech tags or just a sequence of words) and two inputs for a single word (word with part of speech tag or just a word). The algorithm checks whether the input text contains the part of speech tags. If the input is already part of speech that is tagged, two possibilities occur: (1) a single word, (2) a sequence of words. If the input is a single word, the algorithm jumps to step 4. Otherwise, the input is tokenized into a single word, based on the white space in the sequence. The lemma returns only if both word and part of speech are present in the dictionary in the final step. However, in step 1, if the input is not already part of the tagged speech, the automatic part of the speech tagged is assigned to the input (step 2) by the tagger and the rest of the steps are the same.  
\begin{table}
\begin{tabular}{c c} \hline Total number of unique words & **25,000** \\ \hline Total number of words from UrMono corpus & **20,000** \\ \hline Total number of words from Urdu Wikipedia Dump & **5000** \\ \hline Lexical Coverage from UrMono Corpus & **33,500,000 (33.5 million)** \\ \hline Lexical Coverage from Urdu Wikipedia Dump & **7,000,000 (7 million)** \\ \hline \end{tabular}
\end{table}
Table 2: Detailed statistics of the dictionary.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Word** & **English** & **PoS** & **Lemma** & **English** \\  & **Translation** & & **Translation** \\ \hline \hline \(\mathbf{\psi_{m}}\), & Seen & VBF & \(\mathbf{\psi_{d}}\), & Look \\ \hline \(\mathbf{\psi_{d}}\) & Municipal & JJ & \(\mathbf{\psi_{d}}\) & Municipalities \\ \hline \(\mathbf{\psi_{d}}\) & Side & NN & \(\mathbf{\psi_{d}}\) & Side \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sample of the standard dictionary.
# Path Planning of Inspection Robot Based on Improved Ant Colony Algorithm
## 2 Problem Description  
### Establishment of Environmental Model  
The scenario described involves a patrol robot moving on a 2D plane at a constant speed from an initial point to a designated destination. The robot's motion trajectory on a 2D grid-structured map is represented as a series of continuous positions. This grid-basedapproach to environmental mapping is straightforward to implement and intuitive, making it a common technique for representing the surroundings of patrol robots. In this scenario, the environment is divided into a 20 \(\times\) 20 grid, where each grid cell is classified as either an obstacle (represented by a black cell with a value of 1) or free space (represented by a white cell with a value of 0). The operational zone contains O obstacles, and the autonomous patrol vehicle must navigate around these obstacles to ensure a safe and successful journey. The grid map \(Z\) is composed of grid cells \(Z_{ij}\), where \(i\) and \(j\) denote the row and column indices, respectively.  
\[Z=\left\{Z_{ij}\middle|Z_{ij}=S,0,1,E\right\}\]  
In this grid map, \(Z_{ij}=S\) represents the initial matrix of the delivery autonomous vehicle, \(Z_{ij}=0\) represents a free grid, \(Z_{ij}=1\) represents an obstacle grid, and \(Z_{ij}=E\) represents the target grid.