esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments  
### Dataset Construction and Evaluation Metrics  
#### 4.1.1 Dataset Construction  
Given the absence of suitable existing datasets, we constructed an idealized simulated cutting ejection dataset to train the detection head and validate the effectiveness of the proposed integrated detection-tracking-speed measurement framework.  
To simulate realistic operational conditions, the ejection parameters were selected based on typical cutting trajectories observed in drilling environments. Ejection angles were set between 30\({}^{\circ}\) and 75\({}^{\circ}\) to reflect cases ranging from vertical free-fall to strong oblique projection. Initial velocities included both gravity-driven free-fall (0 m/s) and impact-driven ejection (2-6 m/s), corresponding to common release modes of debris. During image acquisition, the camera model is defined with fixed intrinsic parameters, including field of view (FOV), focal length, and pixel size, which are all known a priori. No additional calibration is required. Pixel coordinates in rendered images can be directly mapped to physical coordinates, and projection accuracy has been verified to ensure precise pixel-to-space displacement correspondence for velocity estimation.  
The dataset comprises two parts:  
The first part simulates the ejection of cuttings when a drilling rig operates on a horizontal top rock layer, covering free-fall trajectories and trajectories with varying initial velocities. All samples were generated based on free-fall motion models, assuming camera frame rates of 30 fps and 60 fps to simulate detection under different temporal resolutions. To enrich the dataset's diversity, six types of stones with different materials and particle sizes were used, resulting in 1402 images.  
The second part simulates the cutting ejection when drilling against a vertical rock wall. For simplification, the ejection trajectories were constrained within a single plane, and lateral displacement effects were neglected. The camera's field of view was set to a 2 \(\times\) 2 m area, with frame rates similarly set at 30 fps and 60 fps. This part contributed 2418 images.  
In total, the dataset consists of 3787 images, all annotated following the COCO standard format. Each label is expressed in the form \"stonesspeed:4.5\", where \"stone\" indicates the category and \"speed:4.5\" represents the real-time velocity, separated by a semicolon \",\" to facilitate decoupled processing for detection and speed regression tasks. The overall situation is shown in Table 1.  
This dataset underpins the training and evaluation of the improved detection head, the integration of the ByteTrack tracking algorithm, and the velocity estimation module, ensuring the robustness and generalization capability of the proposed method in small-object and high-speed motion scenarios.  
The experimental environment configuration is summarized in Table 2. During the training of the YOLOv11-Dd model, the number of training epochs was set to 300, with a batch size of 16 images per epoch, and the input image resolution was fixed at \(640\times 640\) pixels.
## 2 Materials and Methods
### Sampling Methodology
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Code** & **Sediments** & \multicolumn{3}{c}{**Chironomids**} & \multicolumn{3}{c}{**Oligochaates**} & \multicolumn{3}{c}{**BSAF**} \\ \hline  & **Field\({}_{\text{China}}\)Oligo** & **Lab\({}_{\text{Ch}}\)** & **Lab\({}_{\text{OH}}\)** & **Ch\({}_{\text{Ind}}\)** & **Ch\({}_{\text{Lab}}\)** & **Ch\({}_{\text{Ind}}\)** & **LvLab** & **Ch\({}_{\text{Ind}}\)** & **Ch\({}_{\text{Lab}}\)** & **Ollind** & **LvLab** \\ \hline PAL-SL & 4.441 \(\pm\) 0.085 & 8.876 \(\pm\) 0.279 & 8.51 \(\pm\) 0.384 & 0.396 & 2.020 & 0.856 & 3.588 \(\pm\) 1.078 & 0.09 & 0.23 & 0.19 & 0.42 \\ PAL-P & 0.233 \(\pm\) 0.020 & 0.353 \(\pm\) 0.015 & 0.334 \(\pm\) 0.014 & 0.125 & 0.214 & 0.278 & 0.169 \(\pm\) 0.012 & 0.54 & 0.61 & 1.19 & 0.50 \\ BAV-SL & 0.137 \(\pm\) 0.003 & 0.181 \(\pm\) 0.026 & 0.132 \(\pm\) 0.006 & 0.302 & 0.249 & 0.134 & 0.445 \(\pm\) 0.391 & 2.20 & 1.38 & 0.98 & 3.37 \\ BAV-P & 0.175 \(\pm\) 0.012 & 0.214 \(\pm\) 0.030 & 0.186 \(\pm\) 0.012 & 0.180 & 0.302 & 0.247 & 0.145 \(\pm\) 0.013 & 1.03 & 1.41 & 1.41 & 0.80 \\ CARM-SL & 0.122 \(\pm\) 0.001 & 0.098 \(\pm\) 0.003 & 0.098 \(\pm\) 0.001 \({}^{\lx@sectionsign}\) & 0.105 & 0.152 & 0.512 & 0.220 \(\pm\) 0.007 & 0.86 & 1.55 & 4.20 & 2.24 \\ CARM-P2 & 0.258 \(\pm\) 0.006 & n.m. & 0.258 \(\pm\) 0.006 \({}^{\lx@sectionsign}\) & n.a. & n.a & 0.270 & 0.153 \(\pm\) 0.006 & n.a. & n.a & 1.04 & 0.59 \\ PAL-U & 6.328 \(\pm\) 0.061 & 4.998 \(\pm\) 0.053 & 4.998 \(\pm\) 0.053 \({}^{\lx@sectionsign}\) & 0.875 & 1.47 & 1.752 & n.a. & 0.14 & 0.29 & 0.28 & n.a \\ LMRE-SL & 1.479 \(\pm\) 0.019 & n.m. & 1.479 \(\pm\) 0.019 \({}^{\lx@sectionsign}\) & 0.964 & n.a. & 0.395 & 0.241 \(\pm\) 0.034 & 0.65 & n.a & 0.27 & 0.16 \\ CNT & n.a. & 0.018 \(\pm\) 0.001 & n.m. & n.a. & 0.279 & n.a. & 0.220 \(\pm\) 0.098 & n.a. & n.a. & n.a. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Total mercury concentrations (mg kg\({}^{-1}\) dry weight) in sediments, and indigenous and laboratory-exposed organisms. Field\({}_{\text{China}}\)Oligo: sediment aliquots for indigenous Hg analysis; Lab\({}_{\text{Ch}}\) and Lab\({}_{\text{OH}}\): sediment aliquots used for bioaccumulation tests on _Chironomis triparis_ and _Lumriculus variegitats_; Ch\({}_{\text{Ind}}\) and Oil\({}_{\text{Ind}}\): indigenous chloronimothromids and diogchoaches; Ch\({}_{\text{Lab}}\) and Lv\({}_{\text{L}}\): _C. triparis_ and _L. unrigeutas_ exposed to tests; BSAF: biota-sediment accumulation factor; CNT: control exposures; n.a. not applicable; n.m.: not measured. Concentrations with 5 were not measured and values from other aliquots were considered for calculation and comparison purposes.
## 3 Numerical Simulation
### Parameter Calibration  
The microscopic features of the UDEC Trigon blocks and the contacts govern the mechanical behavior of the rock masses. Rock mechanical parameters obtained from the laboratory must be calibrated before applying the numerical models to predict the deformation performance of the rock masses. Firstly, the elastic modulus (\(E_{\rm m}\)) of a rock mass was calibrated using the Rock Quality Designation (RQD) measured by field coring and the elastic modulus (\(E_{\rm r}\)) of a small specimen (Equation (1)). Afterwards, the ultimate strength of the rock mass was evaluated according to Equation (2), which was found by [PERSON] and [PERSON] [21] through numerous uniaxial compressive strength (UCS) tests. The estimated parameters of rock mass are provided in Table 2.  
\[\frac{E_{\rm m}}{E_{\rm r}}=10^{0.0186\rm{RQD-1.91}} \tag{1}\]  
\[\frac{\sigma_{\rm cm}}{\sigma_{\rm c}}=\left(\frac{E_{\rm m}}{E_{\rm r}}\right) ^{p} \tag{2}\]  
Then, a 1 m \(\times\) 2 m model was built to carry out a series of uniaxial compression tests with a loading rate of 0.01 m/s. The properties of the blocks and the contacts were determined based on the method suggested by [PERSON] and [PERSON] [16], as shown in Table 3. The elastic moduli of the blocks were consistent with those of the rock masses. The normal and shear stiffness of the contacts, \(K_{n}\) and \(K_{s}\) were estimated from the following equation [22].  
\[K_{n}=n\left[\frac{K+(4/3)G}{\Delta Z_{\rm min}}\right],1\leq n\leq 10 \tag{3}\]  
Figure 6: Model overview.
## 3 Design of Hardware and Software for the Data Monitoring System
### Design of Hardware for the Data Monitoring System
#### 3.1.4 External USB Memory  
In order to simplify the data storage and the final data recording, the data-logger is developed to save all data inside an external memory by the use of a peripheral USB. This memory has to be connected along all the process of data-logging, therefore if there is not a USB memory connected, the data-logger will be unable to save data and the data acquisition will be interrupted.  
In case of a current failure, or the destruction of the microcontroller, it is really easy to recover the data extracted before the breakdown. Also, this technique avoids the necessity of developing a procedure to transmit all the data to a computer or to another USB-PENDRIVE. When we extract data there is no need to turn off the data-logger. A red LED is on when the process of saving data is taking place.  
The requirements of the external memory are not too expensive. All kinds and brands of USB Memories are allowed. Even low level memories around 100 Mb ensure the autonomy of the data-logger for more than one year recording data of 50 sensors and one data per minute.
## 4 Discussion
### Metabolomic Data Analysis of YJ, an Aromatic Rhododend  
With the advancement of molecular biotechnology in plant aroma research, the intricate network of flower aroma biosynthesis is gradually coming into focus. Terpenes, as the largest class of compounds in flower aroma [43], are primarily synthesized via the MEP and MVA pathways. In this study, the contribution of terpenes to flower aroma was assessed through non-targeted metabolomics. Flower fragrance is often a mixture of many compounds, and terpenoids are often the main components that affect flower fragrance. Seven types of metabolites were identified using GC-MS, LC-MS (POS), and LC-MS (NEG), with the majority being terpenes and benzene/phenylpropyl compounds. Other researchers have also obtained similar test results [44; 45; 46; 47]. Both varieties of rhododendrons exhibited a significant number of differential metabolites under the three detection modes, indicating that the transition from flower bud to first bloom is critical for the synthesis of volatile terpenoid metabolites and their precursors. However, the NW variety continued to synthesize non-volatile terpenoid compounds, causing a buildup of these metabolites during decay and thereby influencing aroma differences  
Ten volatile substances accumulate significantly in YJ, nine of which accumulate significantly in YJ2 and YJ3, including terpineol, cis-muurola-4(15),5-diene, (-)-myrtenol, ppinene, bicyclo[3.1.0]hexane, 4-methylene-1-(1-methylethyl)-, myrtenyl acetate, trans-linalool oxide (furanoid), and linalool. The release and accumulation of these floral aroma substances exhibit an overall trend of initially increasing and then decreasing, which aligns with the fragrance release pattern during YJ flowering. Similarly, in the \"fragrant snow palace powder\" variety of plum blossoms, substantial quantities of cinnamyl acetate, cinnamyl alcohol, and benzyl alcohol are released from the petals during blooming, with the relative content of aroma substances gradually increasing throughout flower development, peaking during the blooming period [48]. During the blooming period of jasmine flowers, there is also a significant accumulation of phenylpropanol and terpenoid compounds [49].
## 3 Methods
### Evaluation Factor Selection Method
#### 3.2.2 Maximum Mutual Information Coefficient Method  
Mutual information quantifies the influence of the occurrence of a random event on the occurrence probability of another random event. The maximum mutual information coefficient is a statistical method for assessing the correlation between two random variables based on mutual information [18]. This statistical method is universal, equitable, and symmetric, effectively illustrating the degree of correlation between any two random variables and facilitating a deeper exploration of their relationships. The calculation process is as follows:  
Let \(X=\{x_{1},x_{2},\cdots,x_{N}\}\) and \(Y=\{y_{1},y_{2},\cdots,y_{N}\}\) be two random variables. We construct an ordered pair sample set \(D=\{(x_{i},y_{i})|i=1,2,\cdots,N\}\) distributed in two-dimensional space, where \(N\) represents the sample size. The ranges of variables \(X\) and \(Y\) are divided into a and b subintervals, resulting in \(a\times b\) grid spaces G [19]. The constraint on the total number of grids \(G\) is given by \(a\times b<B(N)\), where \(B(N)=N^{\varphi}\) is a function of the sample size, with \(\varphi\) generally set between 0 and 1, typically at 0.6.  
The mutual information \(\gamma_{M}(D|G)\) for different grid partitioning methods can be obtained as follows:  
\[\gamma_{M}(D|G)=\sum\
olimits_{x\in X,y\in Y}p(x,y)\text{log}_{2}\frac{p(x,y )}{p(x)p(y)} \tag{5}\]  
where \(D|G\) represents the probability distribution introduced when set \(D\) is partitioned into grid space \(G\); \(p(x,y)\) denotes the joint probability density function of the random variables \(X\) and \(Y\); and p(x) and \(p(y)\) denote the marginal probability density functions of the random variables \(X\) and \(Y\), respectively.  
Due to the existence of multiple partitioning methods for grid space \(G\), the maximum value of \(\gamma_{M}(D|G)\) under different partitioning approaches is taken as the maximum mutual information value \(\gamma_{M}(D,a,b)\) for grid space \(G\), as shown in Equation (6). To compare the maximum mutual information under different numbers of grids, it is necessary to normalize the maximum mutual information value \(\gamma_{M}(D,a,b)\) of the grid space \(G\) (Equation (7)) to obtain the maximum mutual information coefficient \(\gamma_{Z,a,b}(D)\), denoted as the most \(MIC(D)\).  
\[\gamma_{M}(D,a,b)=max\{\gamma_{M}(D|G)\} \tag{6}\]  
\[\gamma_{Z,a,b}(D)=\frac{\gamma_{M}(D,a,b)}{\log_{2}\min\{a,b\}}, \tag{7}\]  
\[MIC(D)=\max_{a\times b<B(N)}\{\gamma_{Z}(D)_{a,b}\} \tag{8}\]
# Resilience Analysis Grid-Rasch Rating Scale Model for Measuring Organizational Resilience Potential
## 3 Results
### Responding Item Fit Statistics  
In Appendix A, the item characteristic curves (ICCs) for the ten items (R1-R10) of the responding resilience potential are presented. These ICCs show how the probability of selecting each response category on the Likert-scale rating varied as a function of the underlying latent trait. As shown in Table 2, Item R1 showed infit and outfit mean square (MNSQ) values above the recommended cut-off of 1.4, with an infit MNSQ of 2.94 and an outfit MNSQ of 2.89. The corresponding standardized infit (2.79) and outfit (2.74) statistics also exceeded the \(+2\) threshold, indicating significant underfit. This suggested that R1 was unpredictable within the framework of the model expectations, potentially reducing the overall reliability of the scale and introducing noise into the measurement.  
In contrast, Items R7 and R10 had infit and outfit MNSQ values below 0.6 (infit MNSQ: 0.39 and 0.54; outfit MNSQ: 0.39 and 0.54, respectively). While their standardized infit and outfit statistics (infit ZSTD: \(-1.61\) and \(-1.02\); outfit ZSTD: \(-1.59\) and \(-1.04\)) did not exceed the \(-2\) threshold, the low MNSQ values suggested that these items were overly predictable. This indicated redundancy or limited discriminative power, potentially contributing less useful information to the measurement of the latent trait (i.e., the responding resilience potential).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Statistics** & **Items** & **Persons** \\ \hline Outfit MNSQ Mean & 0.97 & 0.87 \\ \hline Outfit MNSQ SD & 0.72 & 0.23 \\ \hline Outfit ZSTD Mean & \(-0.17\) & \(-0.14\) \\ \hline Outfit ZSTD SD & 1.19 & 0.54 \\ \hline Infit MNSQ Mean & 0.99 & 0.93 \\ \hline Infit MNSQ SD & 0.73 & 0.25 \\ \hline Infit ZSTD Mean & \(-0.13\) & \(-0.09\) \\ \hline Infit ZSTD SD & 1.20 & 0.62 \\ \hline Reliability of Separation & 0.85 & 0.86 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of responding model statistics.  
Item difficulty estimates (\(\delta\)) were examined to indicate each item's position on the latent responding continuum. Most items showed negative difficulty estimates, suggesting they were comparatively easier for organizations to endorse with higher agreement ratings. In particular, R4 (\(\delta=-3.55\)) emerged as the item with the lowest difficulty. Conversely, items with positive difficulty estimates, R1 (\(\delta\) = 0.10) and R6 (\(\delta\) = 0.26), were more difficult to endorse, indicating they required a higher level of the responding trait. Although particular caution is required regarding the interpretations related to Item R1, as it demonstrated high unpredictability, this indicated that many aspects of resilience assessed by the easier items were more readily selected by the organizations, while the two more difficult items represented areas requiring further development.
# Adjacent Vehicle Number-Triggered Adaptive Transmission for V2V Communications
###### Abstract  
For vehicle-to-vehicle (V2V) communication, such issues as continuity and reliability still have to be solved. Specifically, it is necessary to consider a more scalable physical layer due to the high-speed mobility of vehicles and the complex channel environment. Adaptive transmission has been adapted in channel-dependent scheduling. However, it has been neglected with regards to the physical topology changes in the vehicle network. In this paper, we propose a physical topology-triggered adaptive transmission scheme which adjusts the data rate between vehicles according to the number of connectable vehicles nearby. Also, we investigate the performance of the proposed method using computer simulations and compare it with the conventional methods. The numerical results show that the proposed method can provide more continuous and reliable data transmission for V2V communications.  
vehicle communication; vehicle-to-vehicle communications; data transmission method; adaptive modulation scheme +
Footnote †: journal: Sensors
## 2 Results and Discussion
### Synthesis of Core-Corona Polymer, Microsphere-Immobilized Macmillan Catalyst (ICCC)  
The ionic, core-corona polymer, microsphere-immobilized MacMillan catalyst (ICCC) **6** was synthesized by the neutralization reaction of the CCM-SO\({}_{3}\)H **4** with MacMillan catalyst precursor **5** (Scheme 2). For example, **6d\({}_{\bf 20}\)C** was synthesized from **4d\({}_{\bf 20}\)C** and **5**. These results were summarized in Table 2. The degree of catalyst immobilization measured by the recovered catalyst was more than 60%. No side reaction was observed at all. The degree was slightly increased when low-crosslinked polymer microsphere **4d\({}_{\bf 10}\)B** was used instead of that with 20 mol % of crosslinking **4d\({}_{\bf 20}\)C**. The significant improvement was observed when the reaction was conducted in CH\({}_{2}\)Cl\({}_{2}\)/CH\({}_{3}\)OH mixed solvent (entry 7 versus 8) Interestingly, the degree by the core-corona polymer microsphere with longer corona (**6d\({}_{\bf 10}\)B-200**) was higher, possibly because the graft polymer chain was an extended chain rather than a random coil due to the higher M\({}_{\rm n}\) of the corona. The catalyst contents in **6** determined from the mass of the recovered catalyst were in the range of 0.53-1.1 mmol g\({}^{-1}\). The characteristic strong absorption peak at 1721 cm\({}^{-1}\) for C = O in the imidazolidinone moiety was observed from the FT-IR of **6** (Figure 1). The SEM images of ICCC are shown in Figure 2. Since the deprotection of phenylsulfonate of **2**, the acidification of the sodium sulfonate of **3**, and the ionic immobilization between **4** and **5** did not affect the particle diameter, the D\({}_{\rm n}\) (**6**) was unchanged. For example, the D\({}_{\rm n}\) values of **3d\({}_{\bf 10}\)B**, **4d\({}_{\bf 10}\)B**, and **6d\({}_{\bf 10}\)B** were 1.90, 1.92, and 1.91 \(\upmu\)m, respectively. The nitrogen content measured in **6d\({}_{\bf 10}\)B** was comparable to that calculated. From these results, MacMillan catalyst precursor **5** was successfully immobilized onto the pendant position of CCM with ionic bonding by the neutralization reaction to afford ICCC **6**.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Entry** & **4** & \(D_{\bf n}\) (Core) & \multicolumn{2}{c}{**Corona**} & \(D_{\bf n}\) (Core-Corona) & \(U\) (4) & **S Content** \\  & & **(\(\upmu\)m)a  & \(M_{\rm n}\)b  & \(M_{\rm w}\)/\(M_{\rm n}\)b  & \(\left(\upmu\right)\)a  & \(\left(\mbox{mmol g}^{-1}\right)\)c  \\ \hline
1 & **4d\({}_{\bf 20}\)C** & 1.14 & 7630 & 1.18 & 1.34 & 1.00 & 1.00 \\
2 & **4d\({}_{\bf 10}\)B** & 1.08 & 11,000 & 1.32 & 1.40 & 1.23 & 1.61 \\
3 & **4d\({}_{\bf 10}\)B-200** & 1.08 & 30,400 & 1.57 & 1.95 & 1.20 & 1.48 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characterization of CCM-SO\({}_{3}\)H **4**.
# Optimized Score Level Fusion for Multi-Instance Finger Vein Recognition
## 5 Experimental Analysis  
### Experimental Setup  
In this work, two publicly available datasets are used for the experimental evaluation of the proposed methods. The SDUMLA-HMT database is the first dataset used. It is a homologous multimodal traits database from the Group of Machine Learning and Applications, Shandong University (SDUMLA) [16]. The SDUMLA-HMT database contains data from 106 individuals of different biometric traits. The biometric traits available in the database include the face, finger vein, gait, iris, and fingerprint. As the focus of this study is on finger vein recognition, only the SDUMLA-HMT finger vein database is used. The images from the index, middle, and ring fingers of both hands were collected six times for 106 individuals. Therefore, there are a total of 3816 (106 individuals \(\times\) 6 fingers \(\times\) 6 samples) images in the dataset. Each image is stored in a \"bmp\" format with the size of 320 \(\times\) 240 pixels. Some samples of finger vein images in the dataset are shown in Figure 4.  
Figure 4: The sample images of SDUMLA-HMT Finger Vein Dataset.  
University of Twente Finger Vascular Pattern (UTFVP) dataset is the second dataset used for experimental evaluation. The dataset was collected from 60 individuals with 73% male, 87% right-handed, and 82% aged 19-30 at Twente University [17]. The finger vein images were captured with a local custom device. Each individual provides the index, middle, and ring finger of both hands, and each finger is sampled four times. In total, there are 1440 (60 individuals \(\times\) 3 fingers \(\times\) 2 hands \(\times\) 4 samples) images in the dataset. Each of them is in a \"png\" format with 672 \(\times\) 380 pixels in size. The samples of finger vein images from UTFVP can be seen in Figure 5.  
For the subsequent experimental analysis purpose, the following conventions are used: _li_ stands for the left index finger, _lm_ stands for the left middle finger, _lr_ stands for the left ring finger, _linear1_ stands for linear SVM matching without optimization, _linear2_ stands for linear SVM matching with optimization, while _rbf1_ stands for RBF SVM matching with optimization, _rbf2_ stands for RBF SVM matching with optimization, _BSSF_ stands for Bayesian optimized SVM score fusion, and _BSBF_ stands for Bayesian optimized SVM based fusion. Equal error rate (EER) is used as the performance measurement of the proposed methods. EER is the rate at which the false acceptance rate and false rejection rate are (almost) equal. In this work, _linear1_ and _rbf1_ are used to serve as a baseline score level fusion method for comparison purpose.  
The proposed system is tested with stratified three-fold cross-validation to ensure the validity of the system. The optimization of the hyperparameters is also done with the cross-validation using a different random seed from the test cross-validation to prevent overfitting to the test cross-validation [18].