esa-sceva/satcom-chunk-collection huggingface dataset:
# Optical Code Construction of 2D Spectral/Spatial BIBD Codes for SAC-OCDMA Systems
## 3 2D-BIBD System Description  
### Structure of 2D-BIBD System  
The transmitter and receiver modules \((N1\times N2)\) of the 2D-BIBD system encompasse several encoder and decoder arrangements. For a single user, each transmitter modules is  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **R(0)(g,_h_)** & **R(1)(g,_h_)** & **R(2)(g,_h_)** & **R(3)(g,_h_)** \\ \hline \(g=0\), \(h=0\) & \(w_{1}w_{2}\) & \(0\) & \(0\) & \(0\) \\ \(g=0\), \(h\
eq 0\) & \(0\) & \(w_{1}w_{2}\) & \(0\) & \(0\) \\ \(g\
eq 0\), \(h=0\) & \(w_{2}\) & \(0\) & \(w_{2}(w_{1}-1)\) & \(0\) \\ \(g\
eq 0\), \(h\
eq 0\) & \(0\) & \(w_{2}(w_{1}-1)\) & \(0\) & \(w_{2}(w_{1}-1)\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Cross-correlation values of 2D-BIBD codes.  
formed by the combination of a light-emitting diode (LED), a Mach-Zehnder Modulator (MZM) and uniform fiber Bragg gratings (FBGs) filters. A LED, with a broad optical spectrum, is used to provide the required optical spectrum for the encoding operation. The spectrum from the LED is modulated with the help of a MZM as per the binary data generated by the pseudo-random bit sequence (PRBS) source, also referred to as an ON-OFF keying scheme. The MZM modulator converts the electrical signals into optical pulses and delivers them to the FBGs based encoder [14,22].  
Before discussing the structures for the transmitter and receiver modules in detail, it is important to discuss the encoder and decoder design to facilitate the understanding of the proposed 2D-BIBD OCMDA based architecture. The encoder arrangement converts the binary 1s into spectral and spatial representations for every user based on the \(X^{th}\) and \(Y^{th}\) code sequences of the 2D-BIBD code. The encoder module contains two functions: (i) spectral encoding and (ii) spatial encoding. The arrangement of the FBG filters, as shown in Figure 1, is used to perform the spatial encoding of impulses as per the \(X_{h}\) part of the coding scheme. A tree coupler with optical splitters is used to perform the decoding operation. Split parts of the spectrally encoded spectrum are forwarded to the respective star coupler input ports.  
The decoder function is used to recuperate the required encoded spectrum with determined auto- and cross-correlation values. The decoder arrangement includes two operations: (i) spatial decoding and (ii) spectral decoding. The spatial decoder is made up of couplers and optical splitters in reverse order to that of the spatial encoder. Each port of the coupler corresponds to the respective spectral decoder, optical combiners, and two balanced detectors (BD1 and BD2).  
The receiver module is designed using a double-balanced detection technique, and it produces successive codewords using the required spectrum. This technique also suppresses MAI, generated from different spectral code sequences due to the balanced photodiode configuration [14,23]. The system consists of multiple transmitter and receiver sections (\(N1\times N2\)), and each pair uses the \(C_{g,h}\) 2D-BIBD code sequence.
# Unraveling the Scale Dependency of SIF-Based Phenology: Amplified Trends and Climate Responses
## 2 Data and Methods
### Data
#### 2.2.2 Ground-Based Observation Datasets  
As a direct driver of vegetation biomass accumulation, the dynamics of Gross Primary Productivity (GPP) reflect the growth status of vegetation and thus serve as an effective indicator for monitoring and assessing the phenological status of vegetation [47]. In this study, we adopted a phenological extraction method consistent with SIF data to process GPP data and obtained site-specific phenological information, which provides a benchmark for comparisons between different SIF products. The FLUXNET-2015 eddy covariance GPP (GPP) dataset was computed via a standardized flux allocation methodology based on gap-filling net ecosystem exchange (NEE) data [48]. The GPP product used in this  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**DATASET** & \begin{tabular}{c} **SPATIAL** \\ **RESOLUTION** \\ \end{tabular} & \begin{tabular}{c} **TEMPORAL** \\ **RESOLUTION** \\ \end{tabular} & \begin{tabular}{c} **CORE** \\ **METHODOLOGY** \\ \end{tabular} & \begin{tabular}{c} **PRIMARY** \\ **INPUTS** \\ \end{tabular} \\ \hline GOME-2 SIF & 0.5\({}^{\circ}\) & 8-day & \begin{tabular}{c} Radiative transfer inversion, \\ dimensionality reduction \\ \end{tabular} & \begin{tabular}{c} Solar Fraunhofer lines \\ (740 nm band) \\ \end{tabular} \\ GOSIF & 0.05\({}^{\circ}\) & 8-day & OCO-2-trained ML model & \begin{tabular}{c} MODIS vegetation indices, \\ ERA5 meteorology \\ \end{tabular} \\ GRSIF001 & 0.01\({}^{\circ}\) & 8-day & \
\begin{tabular}{c} GR-LGBM spatial ML \\ model \\ \end{tabular} &
\begin{tabular}{c} MODIS reflectance, ERA5 \\ meteorology, and \\ vegetation types \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of SIF datasets used in this study.  
study (GPP_DT_VUT_REF) was derived using the variable USTAR filtering method along with the dayparting method [49]. The daily GPP observations from flux tower sites were aggregated into eight-day intervals to align with the temporal resolution of the SIF data.  
For the selection of deciduous forest flux sites, we followed these criteria: (a) a latitude greater than 30 \({}^{\circ}\)N, (b) observations valid for at least one year during 2008-2017, and (c) the area surrounding the sampling site, with a 0.01\({}^{\circ}\)\(\times\) 0.01\({}^{\circ}\) grid resolution, contained more than 80% deciduous forest, as indicated by the MODIS land cover map. For further details on the chosen flux tower-based GPP sites, please refer to Table S1 in the Supporting Information.
## 5 Simulation Results and Analysis
### Analysis and Comparison of the Proposed Localization Algorithm  
CRLB is the theoretical lower bound on the variance of unbiased estimator [35]. In order to analyze the performance of the proposed three-step WLS closed-form localization algorithm, we compare the algorithm with CRLB of the proposed hybrid positioning, CRLB of TDOA-only positioning and two widely used TDOA-only closed-form localization algorithms, Chan-Ho [17] and BiasRed [18]. CRLB is the trace of the inverse matrix of the [PERSON] information matrix [19].  
\[\mathrm{CRLB}=\mathrm{tr}\left(J^{-1}\right)=\mathrm{tr}\left(\left(A^{T}QA \right)^{-1}\right) \tag{50}\]  
where matrix \(A\) and \(W\) are defined in Section 3.1, \(Q\) is a \(M\times M\) matrix as  
\[Q=\left[\begin{array}{cccc}\sigma_{\mathrm{rad}}^{2}&0&0&\ldots&0\\ 0&\sigma_{\mathrm{t}\mathrm{d}\alpha}^{2}&0&\ldots&0\\ 0&0&\sigma_{\mathrm{t}\mathrm{d}\alpha}^{2}&\ldots&0\\ \vdots&\vdots&\vdots&\ddots&\vdots\\ 0&0&0&\ldots&\sigma_{\mathrm{t}\mathrm{d}\alpha}^{2}\end{array}\right]. \tag{51}\]
## 4 Experiments  
Images captured by multiple cameras were applied to test the performance of the proposed method [21, 22, 23]. Our method was tested on a reduced number of particles in a controlled environment. We compared trajectory lengths obtained from our experimental data with those from real-world scenarios to validate our tracking system's efficacy. This helped us assess its adaptability in capturing the natural behaviors of drosophilas outside controlled environments. We also evaluated the detection accuracy against manually annotated ground-truth data, which helped us measure our detection algorithm's performance in real-world conditions.  
Our experimental setup was meticulously designed to simulate a controlled environment for observing and tracking the flight patterns of drosophilas. The experiments were conducted in a laboratory setting, utilizing a transparent cubic container with an inner dimension of 36 cm to house a specific number of drosophilas. This setup allowed the drosophilas to move freely within a confined space, closely mimicking their natural movement patterns in a relatively open environment.  
To ensure the optimal visibility and contrast of the drosophilas against the background, two orthogonal, flat-panel light sources were placed opposite two high-speed monochrome cameras. This arrangement created a backlit illumination setup, significantly enhancing the silhouette of the drosophilas against the transparent container. The cameras were positioned on two sides of the cube, directly facing the light sources, to capture the drosophilas' movements without interference from direct light.  
In the experimental design, particular attention was paid to controlling environmental conditions to ensure consistency in the recorded behaviors of drosophilas. The experiments were conducted under standardized laboratory conditions with controlled lighting, temperature, and humidity to minimize variations in drosophila activity that could arise from diurnal or seasonal changes. This setup helped us ensure that the behaviors captured in the video recordings were representative of the drosophilas' natural responses to the experimental setup rather than external environmental factors. By maintaining a consistent environment, we aimed to ensure that the observations reflected inherent behavioral patterns of the drosophilas, which are crucial for developing a reliable tracking system.
## 2 Method  
### Hole Detection Method  
The accurate detection of holes is a prerequisite for completing the 3D CHarts. Due to the physical and chemical erosion of the contact surfaces, the fractured surfaces of the fragments are often not completely consistent as they were just broken, resulting in holes on the reassembled 3D CHarts. The holes can be classified as simple holes and complex holes, as shown in Figure 1. Based on the triangular principle, we proposed an effective 3D mesh hole detection method. The details of the method are presented in Algorithm 1.  
```
Initialize. The 3D mesh model of the 3D CHart is composed of a set of triangles, and the triangle contains vertices and edges. Step 1: Traversing the 3d Mesh to get the number of adjacent triangles on each side to form a matrix \(M(E,N,B)\), where \(E\) represents the side, \(N\) represents the number of triangles adjacent to the side, and \(B\) is a Boolean value, which identifies whether the current side has been traversed. Step 2: Find the edge \((v_{1},v_{2})\) whose N value is 1 in \(M(E,N,B)\), which is defined as the hole points set \(\varphi=\{v_{1},v_{2}\}\). Step 3: Utilizing depth-first-search (DFS) to traverse \(M\) along \(\varphi\). Step 4: Adding boundary vertices to the hole points set \(\varphi=\geq\{v_{1},v_{2},v_{3}\}\), and the Boolean value is set to TRUE. Step 5: Repeating Step 4, until a closed loop which is the identified hole is formed. Step 6: Repeating Step 2-Step 4 until the matrix \(M\) does not have hole edges. Output: The hole regions of the 3D mesh model.
```  
**Algorithm 1**Hole detection method for 3D Charts  
Figure 1: Several types of holes existed on the surface of 3D Charts. (**a**) is a simple closed hole formed by connecting the edges of the triangular mesh; (**b**) is a simple hole with a small number of independent triangular meshes in the hole closed by the edges of the triangular mesh; (**c**) is an unclosed complex hole; (**d**) is a simple hole in the scanning data obtained by the scanner.  
Figure 2 illustrates the detection result based on the proposed method described in Algorithm 1, and the hole region can be detected effectively.
## 2 System Description  
The 5G satellite Radio Access Networks (RAN) are envisaged to use Orthogonal Frequency Division Multiple Access (OFDMA) and support multiple antenna transmission schemes. Satellite technology can adopt OFDMA due to the fact that OFDMA allows flexible bandwidth operation with low complexity receivers and easily exploits frequency selectivity [26]. The Multi-User Multi-Input Multi-Output (MU-MIMO) mode that allows simultaneous transmission to multiple users is considered. The 5G New Radio (NR) base station termed gNB is expected to be also suitable for satellite network scenario and is assumed to be located on the earth station. Both the satellite gNB and the User Equipment (UE) are considered to have multiple antennas; each assuming a baseline of \(2\times 2\) MIMO configuration. The 5G NR modulation technique and frame structure are designed to be compatible with LTE. The 5G NR duplex frequency configuration will allow alignment among 5G NR, NB-IoT and LTE-M subcarrier grids [27]. Hence, it is expected that the 5G NR user equipment (UE) will not only exist with 5G satellite networks but also coexist with satellite LTE networks.
## 3 Materials and Methods
### Methods
#### 3.2.1 Text Model Based on the Textual Relation Chain  
In this paper, we proposed a method for the relation chain of different texts. Specifically, we considered labels as a sequence and constructed a chain relationship between labels based on this sequence. It is like the context in a sentence, as shown in Figure 5.  
In order to make this text structure based on chained representations processable by BERT networks, we then adapted the training process of BERT networks.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Task** & **Dataset** & **Publication Year** & **Type** & **Training Set Size** & **Test Set Size** & **Source** \\ \hline \multirow{4}{*}{Cloze tests} & CNN/Daily Mail [15] & 2015 & Narrative writing & 1.2 MB & 56 KB & News report \\  & CBT [16] & 2016 & Narrative writing & 669 KB & 10 KB & Children’s books \\  & LAMBDA [29] & 2016 & Narrative writing & 2 KB & 5 KB & Fiction \\  & CiCR [30] & 2018 & Explanatory article & 91 KB & 7 KB & Clinical reports \\ \hline \multirow{2}{*}{Multiply choice} & MCTest [14] & 2013 & Narrative writing & 1480 KB & 840 KB & Virtual stories \\  & RACE [22] & 2017 & Multiply types & 88 KB & 5 KB & English tests \\ \hline \multirow{2}{*}{Span extraction} & SQuAD [17] & 2016 & Explanatory article & 87 KB & 10 KB & Encyclopedia \\  & NewsQA [19] & 2016 & Narrative writing & 108 KB & 6 KB & News \\  & Trivia [21] & 2017 & Multiply types & 582 KB & 73 KB & Encyclopedia and networks \\ \hline \multirow{2}{*}{Free answering} & MSMARCO [18] & 2016 & Multiple types & 100 KB & 100 KB & Network texts \\  & SearchQA [31] & 2017 & Multiple types & 100 KB & 27 KB & Network texts \\ \cline{1-1}  & Dureader [32] & 2017 & Multiple types & 202 KB & 10 KB & Network texts \\ \hline \hline \end{tabular}
\end{table}
Table 1: Public datasets for machine reading comprehension.  
Figure 5: Textual relation chain.  
The model was first trained to learn the generic text representation by MLM (Mask Language Model) and NSP (Next Sentence Prediction) using a large-scale unlabeled corpus. Then the model parameters were fine-tuned on a specific downstream task dataset. In this training paradigm, the model parameters are no longer randomly initialized but have been integrated with generic knowledge. BERT has Base (12-layer Transformer as the encoder) and Large versions (24-layer Transformer as the encoder) according to the size of the model parameters, and cased (case sensitive) and uncased (case insensitive) versions according to whether they are case-sensitive or not, as shown in Figure 6.
# Soybean Weed Detection Based on RT-DETR with Enhanced Multiscale Channel Features
## 3 Results
### Ablation Experiment  
In order to verify the superiority of the proposed CGF-Block module, EA-AIFI and EMFF network, this paper uses rtdetr as the baseline to ablate each module sequentially. These experiments were all conducted on the dados_tcc dataset [48], in which different experimental groups had a batch size of 1 when testing FPS. The experimental results are shown in Table 4.  
According to the results of Table 4, when the fused CGF-Block module, EA-AIFI and the newly constructed EMFF network are used to replace the corresponding modules in baseline, although the accuracy is improved, they all have certain disadvantages compared with baseline. For instance, the FPS of the CGF-Block module and EA-AIFI were significantly lower than the baseline; moreover, EMFF's GFLOPs were 12% higher than the baseline. However, the three modules correspondingly replace the modules in the baseline, and it can be seen that while the GFLOPs and parameter count are basically the same as the baseline, not only the mAP50 and mAP50:95 have increased by 3.2 and 2.2 respectively, but the FPS has also increased by 4.5 frames. The results of ablation experiments fully prove the success of the multi-scale channel feature optimization model proposed in this paper in soybean weed detection. In order to better show the results of the model in this paper and the benchmark model on the dataset, three pictures were randomly selected, and the results of the two were visualized. From the visualization results in Figure 7, it can be seen that the benchmark model has missed detection phenomenon, especially in the recognition of small targets, and the recognition rate does not cover the whole picture. This also highlights the accuracy of the model proposed in this paper. The main reason is that the model proposed in this paper focuses on fusing multi-scale features through multi-channels, fully combining local features and global context, thereby improving the detection of weeds by the model, especially for small targets.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Model** & **CGF-Block** & **EA-AIFI** & **EMFF** & **Precision (\%)** & **Recall (\%)** & **mAP50 (\%)** & **mAP50 95 (\%)** & **GFLOPs (G)** & **Paras (M)** & **FPS** \\ \hline Baseline & & & & 87.8 & 78.4 & 85.2 & 75.6 & 57 & 19.9 & 45.6 \\
1 & ✓ & & 91.1 & 79.2 & 87.3 & 76.4 & 46.2 & 15.4 & 39.1 \\
2 & & ✓ & & 90.5 & 81.2 & 87.7 & 76.8 & 57.5 & 20.0 & 43.3 \\
3 & & & ✓ & 89.9 & 78.8 & 86.9 & 76.5 & 63.7 & 21.3 & 47.5 \\
4 & ✓ & ✓ & & 92.6 & 80.8 & 87.9 & 77.3 & 46.4 & 15.4 & 48.9 \\ Ours & ✓ & ✓ & ✓ & 92.7 & 81.8 & 88.4 & 77.8 & 52.5 & 18.0 & 50.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of ablation experiments.
# Digital Twins in the Automotive Industry: The Road toward Physical-Digital Convergence
## 1 Introduction  
### Definition of a Digital Twin  
A digital twin is a nonphysical model that has been designed in order to accurately reflect an artificial or physical system, where sensors are placed to acquire a variety of data regarding different aspects concerning the performance of the system. These data are then transmitted to a data-acquisition system and applied to the digital copy. When the digital copy is updated with the relevant data, the virtual model may be used for the implementation of various simulations, which can lead to potential improvements, by creating valuable information that can then be applied back to the original system existing in the physical world. In this way, physical processes and products together with their accompanying elements can be digitally transferred and described in a cyber-world context [1; 2; 3; 4].  
While digital twins have great potential, their use is not a necessity for every fabricated product. Not all objects can be considered as complicated enough in order to need the intense and tactic flow data sensors required by digital twins, neither is it always worth it from a financial point of view to invest important resources for the creation of a digital twin. Therefore, the industrial sectors that have a need to employ digital twins are those that develop and produce products of the niche sector. The rapidly growing digital twin market suggests that while digital twins are already used in many industries, their demand will continue to escalate in the immediate future [5; 6; 7; 8].  
The major benefits of a nonstatic and highly realistic digital model of a physical object are practically unlimited. Understanding individual aspects of real-life objects as well as designing new ones are endorsed and the competence to simulate and optimize is elevated. Via recording and digitally acquiring the operating data of the model (digital twin), in areal-time situation, the recording of behavioral patterns of the real-world system becomes more achievable. Therefore, users can predict them in a greater degree.  
In order to utilize \"digital twin\" technology, three distinct steps are basically needed. At first, the digital twin prototype should be made, taking into account data along with processes carried out in the real-word system. After that stage, a digital twin of the properties of every object must be compiled. At the end, every relevant systemic property should be attributed, so that, via all the data acquired, a further understanding and prognosis can be achieved. In conclusion, the digital twin should be able to realistically represent its physical real-world twin and be able to show its condition in real-time. In order for this to be feasible, the employment of innovative technologies is requested, which are considered as the foundations of today's industrial revolution context. These can be viewed in Figure 1.
## 3 Results and Discussion  
### Stress Contours around Flange Structure  
Contours of the axial stress component around the flange structure are shown in Figure 3 for the finite element model presented in Figure 2. As can be observed in Figure 3a, shortly after the striker impact (1.375 \(\upmu\)s), both the compressive and tensile stresses form simultaneously in the flange and incident bar, respectively. The compressive wave travels towards the left and right sides of the impact plane. The tensile stress enters the incident bar portion from the moment of impact before the impact signal reaches the left end of the structure, where the compressive stress reflects to a tensile wave. Unlike the contour in Figure 3a (1.375 \(\upmu\)s), the contours at 3.392 \(\upmu\)s (Figure 3b), 5.337 \(\upmu\)s (Figure 3c), and other later moments (not shown) are fairly complicated; the stress transfer mechanism in the flange structure can hardly be investigated solely using such complicated contour diagrams. Therefore, with the notion of the observation in Figure 3a, we investigated the mechanism using  
Figure 2: Finite element model near the flange structure region for the case where \(A_{\text{s}}/A_{\text{b}}\) = 1, \(A_{\text{s}}\) = \(A_{t}\), \(L_{t}\) = 20 mm, and \(D_{\text{b}}\) = 20 mm.  
the stress profiles monitored at the strain gauge position of the incident bar (next section), which is usually measured experimentally.
###### Abstract  
This study outlines a technical framework for Internet of Things (IoT) communications on Mars, leveraging Long Range (LoRa) technology to connect Martian surface sensors and orbiting satellites. The designed architecture adapts terrestrial satellite constellation models to Martian environments and the specific needs of interplanetary communication with Earth. It incorporates multiple layers, including Martian IoT nodes, satellite linkage, constellation configuration, and Earth communication, emphasizing potential Martian IoT applications. The analysis covers four critical feasibility aspects: the maximum communication range between surface IoT nodes and orbiting satellites, the satellite constellation's message processing capacity to determine IoT node volume support, the communication frequency and visibility of IoT nodes based on the satellite constellation arrangement, and the interplanetary data transmission capabilities of LoRa-based IoT devices. The findings affirm LoRa's suitability for Martian IoT communication, demonstrating extensive coverage, sufficient satellite processing capacity for anticipated IoT node volumes, and effective data transmission in challenging interplanetary conditions. This establishes the framework's viability for advancing Mars exploration and IoT in space exploration contexts.  
IoT; LoRa; Mars; satellite; NewSpace; framework; feasibility study Article