esa-sceva/satcom-chunk-collection huggingface dataset:
# Matching Consignees/Shippers Recommendation System in Courier Service Using Data Analytics
## 3 Methodology
### Step 3: Prediction of Shippers/Consignees Matching  
A C 4.5 decision tree was used to identify consignees' behavior using R, F, M, NC, NP, W, and D variables to predict shipper cluster suggestions. C 4.5 generates a tree by splitting the given data. It calculates overall entropy and information gain for all attributes. The attribute with the highest information gain is then chosen for the decision. At each node of the tree, C 4.5 chooses one attributethat most effectively splits the training data into subsets with the best cut-off point, according to entropy and information gain [22], as per Equations (5) and (6):  
\[Entropy\left(t\right)=-\sum p\binom{i}{t}\log_{2}p\binom{i}{t} \tag{5}\]  
\[Information\ Gain\left(\
abla\right)=Entropy\left(parent\ node\right)-Entropy \left(child\ node\right) \tag{6}\]  
where \(p\binom{i}{t}\) is the fraction of records belonging to class i at a given node t [67]. The process used was as follows:  
1. Classification of the delivery behavior of X==>Y consignees and Y shipper clusters through a decision tree algorithm. Predictions of X consignees were used as testing data (Figure 2).
2. Evaluation of the classification model through 10-fold cross-validation. Accuracy and confidence of rule thresholds were determined for the expected value of minimum revenue in Step 4.
## 2 Materials and Methods
### Measurements of Morphological and Biochemical Attributes  
The lettuce's fresh head weight (HW, g), plant diameter (PD, cm), and plant height (PH, cm) were measured at harvest, that is 52 (first growing cycle) and 53 (second growing cycle) days after planting. Furthermore, root weight (RW, g), root diameter (RD, mm) and root length (RL, cm), steam diameter (SD, cm), and leaf number (LN, n) as well. At the same time, the fresh leaf samples were taken and placed in a freezer at \(-80\)\({}^{\circ}\)C for determination of biochemical attributes. All measurements were taken on the same plant. For the proline and MDA determination, five leaves were taken from each group treatment, joined, and ground in liquid nitrogen using a pestle and mortar. Around 100 mg of fine powder was transferred into Eppendorf tubes. Each treatment had three repetitions.  
The extent of membrane damage was verified by malondialdehydedehyde (MDA) determination using thiobarbituric acid (TBA) reaction [65]. Leaves (100 mg) were homogenized in 1.2 mL of reaction solution [0.3% (\(w/v\)) (TBA) in 10% (\(w/v\)) trichloroacetic acid (TCA)] and heated at 85\({}^{\circ}\)C for 45 min. The reaction was stopped by cooling in an ice bath for 20 min, and the mixture was centrifuged (20,000\(\times g\) for 20 min). Absorbance was read from the supernatant at 532 nm and the corrections for the non-specific turbidity were made by taking away the absorbance at 600 nm. TBA in 10% TCA solution represented a blank. The concentration of MDA was expressed in micromols per gram of fresh weight [umol g\({}^{-1}\) FW] using an extinction coefficient of 155 mM\({}^{-1}\) cm\({}^{-1}\)).  
Leaf tissue (100 mg) was homogenized in 2 mL of 3% (\(w/v\)) sulfosalicylic acid and the supernatant was obtained by centrifugation at 15,000\(\times g\) for 20 min. Acid ninhydrin and glacial acetic acid were joined to the supernatant and the reaction mixture was heated at 100\({}^{\circ}\)C for 1 h. The reaction was interrupted by cooling in an ice bath. After the addition of toluene, the absorbance of free proline separated in the upper phase at 520 nm was recorded [66]. Proline concentration was determined using a calibration curve in the range of 25-300 uM and expressed as micromols per gram of fresh weight [umol g\({}^{-1}\) FW].
# End-to-End Object Detection with Enhanced Positive Sample Filter
## 1 Introduction
To provide richer information, we add another binary classification branch regardless of category that just aims at distinguishing foreground samples from backgrounds. The learning of classification will constrain the model to focus most on the intrinsically high-level representations of objects instead of the scale-aware representation. Thus, we can concatenate features from the regression branch and the newly added binary classification branch to produce a more robust feature representation, which can provide compensatory clues for positive sample selection and we call this Dual-stream Feature Enhancement (DsFE). To enhance the feature distinction in potential foreground regions, we propose a Disentangled Max Pooling Filter (DeMF), where a residual stream, a first max pooling stream and a second max pooling stream are constructed to jointly help generate a more reliable local feature representation. Specifically, the second max pooling steam introduces local secondary-maximum values to revise the activations in local regions. The proposed DsFE and DeMF are combined together to provide an adaptive positive sample filter on the predicted _C-d_ (C = 80 for COCO, C = 20 for PASCAL VOC, C = 1 for CrowdHuman and Caltech) classification logits to obtain final NMS-free classification outputs. We call the combined module an Enhanced Positive Sample Filter (EPSF). Equipped with it, our end-to-end object detector outperforms existing one-stage end-to-end detectors without introducing much complexity. Our main contributions can be summarized as follows:  
* We propose an end-to-end object detector that eliminates NMS post-processing and realizes fully end-to-end detection. We specifically design an Enhanced Positive Sample Filter (EPSF) for the adaptive positive sample selection in local areas, which is realized by two components, i.e., DsFE and DeMF;
* We design a Dual-stream Feature Enhancement module (DsFE) that extracts rich information from features learned with different targets for one-to-one classification;
* We design a Disentangled Max Pooling Filter (DeMF) to enhance the feature discriminability in potential foreground areas via disentangled max pooling;
* Our proposed end-to-end object detector achieves a competitive performance against many state-of-the-art detectors. Extensive experiments on COCO [15], PASCALVOC [16], CrowdHuman [17] and Caltech [18] datasets validate the effectiveness of the proposed method.
# Involvement of the Orexinergic System in Cancer: Antitumor Strategies and Future Perspectives
## 3 Orexins and Cancer
### Endomet Cancer  
OXR2 expression has been studied in human endometrial endometrioid carcinoma (EEC) and in normal endometrium; OXR2 was detected in normal endometrial epithelia but frequently lacking in EEC, and the latter observation was related to OXR2 hypermethylation [58]. This suggests that an OXR2 epigenetic silencing occurs in EEC. Moreover, this hypermethylation was correlated with a weak OXR2 expression in endometrial cell lines (ECC-1, MFE-280, Ishikawa), in which neither orexin A nor orexin B changed the proliferative activity.
## 5 Machine Learning for the Prediction Model
### Results from Machine Learning
#### 5.3.2 Verification of Prediction Model for Raft Settlement  
In this study, raft settlement (\(S_{R}\)) and pile settlement (\(S_{P}\)) were predicted corresponding to adjacent tunneling to a piled raft. Through this, the change rate of settlement along the pile-length prediction model was developed. Then, to determine the reliability of the predictions, the \(S_{R}\) prediction was compared and analyzed with the laboratory model test results, since \(S_{P}\) could not be measured in the laboratory model test.  
In the verification process of the models, the results of the laboratory model test were only inputted to prediction model_\(S_{R}\) as training data, and the comparison of the predicted \(S_{R}\) and actual values is shown in Figure 14. The raft-settlement prediction model in the laboratory model test using prediction model_\(S_{R}\) showed an RMSE of 0.02 for the XGB algorithm; nevertheless, MLP was 8.23, which was significantly inaccurate. The RMSE of MLP was due to an imbalance in the number of samples, because those in the laboratory model test were 15, which is considered very small compared to the number of data used in the actual training.  
Figure 13: Prediction model_dS.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Prediction** \\ **Model** \\ \end{tabular} } & \multicolumn{2}{c}{**XGB**} & \multicolumn{2}{c}{**MLP**} \\ \cline{2-5}  & **Training** & **Test** & **Training** & **Test** \\ \hline \(S_{R}\) & 2.156 & 0.039 & 2.440 & 0.044 \\ \(S_{P}\) & 1.632 & 0.030 & 2.321 & 0.043 \\ dS & 0.048 & 0.035 & 0.075 & 0.055 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of RMSE for prediction models.  
However, XGB had significantly less of an effect, which indicated that XGB is more reliable than MLP. The cause of this will be compared by obtaining more verification data.
## 5 Conclusions  
R-SpaceFibre, a modified implementation of the SpaceFibre standard with smaller footprint and limited features, is presented in this paper. A brief analysis of satellite on-board high-speed serial links state-of-the-art is given, pointing out the reasons that lead ESA to support the development of SpaceFibre protocol. The idea of having a high-speed protocol with lower resource utilisation is exploited in some application scenarios, proposing R-SpaceFibre as a suitable solution. Starting from SpaceFibre standard analysis, the design choices behind R-SpaceFibre are described, showing and analysing design trade-off functionalities versus complexity. Resource utilisation results on space-grade devices are presented and compared with the one of a complete SpaceFibre implementation, showing that a resource reduction above 40% is achieved. A hardware validation set-up is then presented and two different testing configurations are described, demonstrating R-SpaceFibre compatibility with a complete version of the CODEC. Finally, CubeSat use case is analysed, highlighting how R-SpaceFibre represents an enabling technology for small satellite high-speed communication links, increasing data-rate and limiting the overall mass and volume of the electronic system.  
The research and the article preparation were carried out by [PERSON] and P.N. under the supervision of [PERSON] and [PERSON].  
IngeniArs SpaceFibre technologies have been developed in the framework of the SIMPLE project (Spacefibre IMPLementation design & test Equipment). This project received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No. 757038.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Solution** & **Number of Cables** & **Mass (g/m)** \\ \hline R-SpaceFibre & 1 & 15 \\
4 SpaceWire & 4 & 168 \\ \hline \hline \end{tabular}
\end{table}
Table 5: R-SpaceFibre interface vs. four SpaceWire interfaces cable mass.
## 4 Empirical Assessment Using Real-World Data
### Results
#### 4.3.4 Model Interpretability
The gradient or color intensity represents the range or distribution of values for the secondary feature, allowing for a multi-dimensional analysis within the same plot. This study only considers the main feature of each PDP in our interpretation. The AADT plot shows a clear positive correlation. As the volume increases, so does the impact on crash frequency, as depicted by rising SHAP values. This is expected, as more traffic typically increases the likelihood of accidents. The PDP for suburban roads indicates a distinction in crash frequency when compared to rural roads. There is a clear jump in SHAP values for suburban areas, which could be due to higher traffic volumes, intersections, and varying road-user behavior in suburban settings compared to rural areas. The PDP for road segment length indicates a non-linear relationship with the crash frequency. Initially, as the length increases, the SHAP value increases, suggesting a higher crash frequency. However, beyond a certain point, the SHAP values decrease, implying that longer road segments may not always correlate with higher crash frequencies. The presence of high SHAP values for shorter lengths may be associated with urban areas where intersections and traffic are more frequent, potentially leading to more accidents. Wider shoulders on roads show a mix of positive and negative SHAP values, which suggests that there is not a straightforward relationship between shoulder width and crash frequency. It is possible that very wide shoulders might sometimes be misinterpreted by drivers as an extra lane or may encourage stopping, leading to accidents, while in other contexts they provide a safety margin. The introduction of a climbing lane generally shows a positive SHAP value, indicating an increased crash frequency. This might reflect more complex road geometries or changing driver behaviors such as overtaking in these areas. The presence of a median barrier seems to have a mixed impact on the crash frequency. Some points indicate a decrease in crash frequency (negative SHAP values), possibly due to the barrier preventing crossover accidents, while others show an increase (positive SHAP values), which could be due to collisions with the barrier itself or other complexities that are not captured by this variable alone. The effect of street lighting on crash frequency is not clear-cut. While one might expect improved visibility to reduce crashes, the PDP suggests a mixed relationship, where the effect may vary based on other factors such as road type, traffic volume, or driver complacency in well-lit areas. The PDP plot for lane width indicates that varying lane widths have a diverse impact on crash frequency, with no clear trend. This implies that lane width alone is not a consistent predictor of crash frequency and must be considered in conjunction with other road features.
## 6 Examples
### Example 5: Densely Clustered Non-Symmetric Linear System  
Consider an example with \(a_{ij}=\left(i+j/2\right)/n,\ i,j=1,\ldots,n\), where \(n=2000\). Suppose that \(x_{i}=1,\ i=1,\ldots,2000\) are exact solutions, and the initial values are \(x_{i}^{0}=0\), \(i=1,\ldots,2000\). The values of matrix elements are densely clustered in a narrow range with \(a_{ij}\in[0.00075,1.5]\). This problem is a highly ill-conditioned non-symmetric linear system with the condition number over the order \(10^{15}\). Table 4 compares the maximal errors (MEs) and number of steps (NS) for different methods under \(\varepsilon=10^{-10}\). \(m_{0}=1\) is used in OMPA and A-GMRES; \(m_{0}=1,m_{1}=3\) are used in current GMRES(m), and \(m_{0}=2,m_{1}=3\) are used in ROGMRES(m). ROGMRES(m) is convergent faster than other algorithms. Even for this highly ill-conditioned problem, the proposed novel methods are highly efficient in finding accurate solutions.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Method** & **OMPA** & **A-GMRES** & **Current GMRES(m)** & **ROGMRES(m)** \\ \hline NS & 4 & 3 & 9 & 2 \\ \hline ME & \(2.10\times 10^{-9}\) & \(1.46\times 10^{-11}\) & \(1.18\times 10^{-13}\) & \(5.98\times 10^{-13}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: For example 5, ME and NS obtained by different methods.  
Figure 4: For example 4, the residuals obtained by OMPA, A-GMRES, ROGMRES(m), original GMRES(m), and current GMRES(m), which is coincident with that obtained by ROGMRES(m).  
Figure 5: For example 4, \(\eta\) obtained by OMPA and A-GMRES. If the automatically orthogonality preserving method is not taken into account, they fail.
# Instance Hard Triplet Loss for In-video Person Re-identification
## 5 Conclusions  
In this paper, we investigated the in-video person ReID task. To address this problem, we proposed a new large-scale video-based in-video ReID dataset, PoseTrack-ReID, with full images available. We also proposed a ReID-Head network, which could incorporate both one-stage and two-stage human detectors, realizing a real-time multi-person ReID feature extracting with reused features. We designed a novel instance hard triplet loss, which could be applied in both cross-camera and in-video ReID problems even with an indefinite quantity of persons and bounding boxes. An unsupervised  
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Training Data** & **Test Bbox** & **G = 1** & **G = 5** & **G = 10** & **G = 15** \\ \hline Human-labeled & detected & 40.0 & 39.2 & 37.8 & 35.9 \\ RIA-generated & detected & 40.2 & 39.2 & 37.9 & 36.4 \\ \hline Human-labeled & labeled & 99.5 & 93.6 & 87.1 & 81.1 \\ RIA-generated & labeled & 99.4 & 92.6 & 84.8 & 76.9 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of models trained on human-labeled data and Reciprocal Identity Association (RIA)-generated data, respectively, on the PoseTrack-ReID dataset. Rank-1 accuracy is reported. Results on both detected bounding boxes and labeled bounding boxes are reported.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **G = 1** & **G = 5** & **G = 10** & **G = 15** & **t/bbox** & **t/img** \\ \hline PCB [2] & 20.2 & 20.6 & 20.9 & 20.8 & 10.5 ms & 14.7 ms \\ ReID-Head & 40.0 & 39.2 & 37.8 & 35.9 & 1.7 ms & 1.7 ms \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of PoseTrack-ReID for the in-video ReID task with the state-of-the-art PCB [2] model. Feature extracting time t on an NVIDIA Titan Xp GPU for a bounding box and for an image with 10 bounding boxes are also provided. Rank-1 accuracy is reported. PCB, Part-based Convolutional Baseline.  
reciprocal instance association method was also proposed so that we could obtain an in-video model in an unsupervised way, further extending the potential applications of in-video ReID. Next, we will integrate the in-video ReID method into a human tracking system to improve its performance.  
Conceptualization, X.F.; methodology, X.F. and W.J.; software, X.F.; validation, X.F., H.L. and W.J.; formal analysis, X.F., W.J., and W.M.; investigation, H.L.; resources, W.J., [PERSON], and H.Y.; data curation, X.F.; writing, original draft preparation, X.F.; writing, review and editing, [PERSON], H.L., and W.J.; visualization, X.F.; supervision, W.J., W.M. and H.Y.; project administration, W.J., W.M., and H.Y.; funding acquisition, W.J., W.M., and H.Y. All authors read and agreed to the published version of the manuscript.  
Supported by the National Natural Science Foundation of China (No. 61633019), the Public Projects of Zhejiang Province (No. LGF18F030002), and the Science Foundation of Chinese Aerospace Industry (JCKY2018204B053).  
The authors declare no conflict of interest.
## 2 The ProbPlot via IR-AVPCA Technique for Bearing Fault Detection and Diagnosis (BFDD)
### Discussion
#### 3.3.1 Under Constant Speed Environment

Table 3: Summary of proposed ProbPlot via IR-AVPCA-based BFDD efficiency for the different features (images) generation methods using PCA and AVPCA algorithms under both constant and variable speed environments. 96.44% success rate for fault diagnosis) or directly considering the image of the raw vibration signal (78.67% success rate for fault detection and 76.89% success rate for fault diagnosis).
# Positioning by Multicell Fingerprinting in Urban NB-IoT Networks +
###### Abstract
The main contributions of this paper can be summarized as follows:  
* It investigates the feasibility of RF fingerprinting in NB-IoT networks and assesses its accuracy in two urban scenarios by leveraging two large-scale measurement campaigns executed in the cities of Oslo, Norway, in 2019 [3] and Rome, Italy in 2021;
* It proposes several strategies for designing the W\(k\)NN-based online phase that combines network coverage and RF signal information;
* It assesses the performance of the above strategies by taking into account (a) the RF parameter adopted in the definition of fingerprint, (b) the amount of RPs collected in the offline phase, and (c) the number of cells considered in the fingerprinting system;
* It studies the impact on positioning accuracy of combining data from multiple operators and data preprocessing to remove fast fading and discusses the adoption of data combination and preprocessing as a means to achieving a favorable trade-off between offline phase efforts and positioning accuracy;
* It provides a detailed description of the data used in the performance evaluation and information on how to access the datasets through an open-source license. The investigation was made possible thanks to the nature of the dataset, in which four RF parameters (RSSI, reference signal received power (RSRP), reference signal received quality (RSRQ), and signal to interference plus noise ratio (SINR)) are provided in a large number of locations and for all the cells detected in such locations, thus overcoming the limitations described in [7]. The paper is organized as follows. Section 2 reviews positioning approaches proposed for NB-IoT. Section 3 introduces the proposed fingerprinting system by first reviewing the W\(k\)NN algorithm and by then describing the proposed strategies. Section 4 provides a detailed description of the measurement setup adopted for the collection of the two datasets used in this work and of the dataset structure. Next, Section 5 presents the results of the performance evaluation of the proposed strategies on the two datasets and introduces two approaches based on the combination of data by multiple operators and on data smoothing to improve accuracy. Section 6 draws conclusions and suggests future research avenues.
# Development of Analytical Model for Bonding of CFRP Rod in Concrete Subjected to Cyclic Loads
## 3 Development of Finite Element Model for G40 Concrete and UHPFRC Using Non-Interface Element
### Finite Element Analysis Result and Discussion  
The simulations were replicated for Grade 40 concrete as well as UHPFRC. The cyclic load was applied to the CFRP embedded within the top concrete, while the bottom concrete remained restrained on the ground. The results shown in Figure 5(a) depict the areas and nodes subjected to the highest displacement, and Figure 5(b) illustrates the accumulated stress distribution. The magnitude of plastic strain is visualised in Figure 6(a) and the equivalent plastic strain is displayed in Figure 6(b).  
An analysis of the load-displacement curve presented in Figure 8 reveals significant insights into the behaviour of G40 concrete under cyclic loading. Notably, the force reaches a peak of 33.66 kN at a displacement of 6 mm. In contrast, the load-displacement curve for UHPFRC specimens exhibits more promising results. The force-displacement relationship indicates a resilient cyclic loading response accompanied by substantial energy dissipation. While the maximum force approaches 70 kN at an 9.14 mm displacement, the bond remains intact, and debonding occurs on average at 9.39 mm. Despite the force diminishing due to debonding at the tip of the CFRP in the bottom concrete, the surface of the CFRP rod in contact with UHPFRC continues to withstand the cyclic loading.  
Figure 6: Numerical Analysis (Abaqus): (**a**) Displacement Magnitude; (**b**) Strain and Stress.  
Figure 7: Numerical Analysis (Abaqus): (**a**) Magnitude of Plastic Strain; (**b**) Equivalent plastic strain.