esa-sceva/satcom-chunk-collection huggingface dataset:
# Failure-Robot Path Complementation for Robot Swarm Mission Planning
## 2 Experimental Vehicle and System Architecture  
### System Architecture  
As the aim was to complete the task with multiple target points using multiple vehicles in the shortest time required, a MAS under a central control was created for this research. The central control allocated assignments to each individual robot and allowed them to complete the tasks independently rather than controlling the cars throughout the experiments. Through cooperation between multiple agents, the system was able to complete larger scale tasks that could not be completed by a single vehicle. From the MAS viewpoint of task allocation, the system consists of single-robot tasks, single-task robots, and the instantaneous allocation of tasks (ST-SR-IA) [14].  
Various assignments at different levels of difficulty were randomly allocated by the system; this means that each robot car could receive any assignment regardless of the difficulty. Thus, each car in the robot swarm had to be equipped with identical specifications to ensure its performance and capacity to cope with various assignments. From the viewpoint of MAS heterogeneity, the degree of similarity between individual robots within a collection \(Het(R)\) was as follows:  
\[\mathrm{Het(R)}=-\sum_{i=1}^{\mathit{Caste}}p_{i}\log_{2}(p_{i}) \tag{1}\]  
where \(\mathit{Caste}\) is the type of robot, and \(p_{i}\) is the decimal percent of a robot belonging to any case. All specifications in this system are the same (\(\mathit{caste}=1\), \(p_{i}=1\)), and \(\mathrm{Het(R)}=0\) (Equation (1)) [15]. Thus, this system is a homogeneity system of a robot swarm.  
Figure 2 illustrates the full system, which includes a remote monitoring station, a multi-vehicle path programming system, and robot cars, and Figure 3 illustrates the system architecture. The remote monitoring station is a laptop with CI7, 2.4 GHz, and 8 GB RAM. It serves as a central controller that can program and distribute the paths to the robot swarm, which is designed to complete the work together. From the viewpoint of network topology, this system is similar to a star topology. The system mainly uses LabVIEW to develop a portable remote monitoring station interface by sending a URL to obtain the Google Maps web page as a display interface. It can be used as a man-machine interface (MMI), where vehicle location feedback and the initial path programming could be displayed and managed. By clicking on the target points displayed on the MMI, the location is sent to the portable remote monitoring station where the path programming algorithm is running. Then, the planned paths can be passed to each car via XBee. Once the cars receive the data, they can engage the brushless motor to move forward and control the servo motor differential to complete the assignment successfully.
# Self-Supervised Learning across the Spectrum
###### Abstract  
Satellite image time series (SITS) segmentation is crucial for many applications, like environmental monitoring, land cover mapping, and agricultural crop type classification. However, training models for SITS segmentation remains a challenging task due to the lack of abundant training data, which requires fine-grained annotation. We propose S4, a new self-supervised pretraining approach that significantly reduces the requirement for labeled training data by utilizing two key insights of satellite imagery: (a) Satellites capture images in different parts of the spectrum, such as radio frequencies and visible frequencies. (b) Satellite imagery is geo-registered, allowing for fine-grained spatial alignment. We use these insights to formulate pretraining tasks in S4. To the best of our knowledge, S4 is the _first_ multimodal and temporal approach for SITS segmentation. S4's novelty stems from leveraging multiple properties required for SITS self-supervision: (1) multiple modalities, (2) temporal information, and (3) pixel-level feature extraction. We also curate m2s2-SITS, a large-scale dataset of unlabeled, spatially aligned, multimodal, and geographic-specific SITS that serves as representative pretraining data for S4. Finally, we evaluate S4 on multiple SITS segmentation datasets and demonstrate its efficacy against competing baselines while using limited labeled data. Through a series of extensive comparisons and ablation studies, we demonstrate S4's ability as an effective feature extractor for downstream semantic segmentation.  
SITS; foundational models; self-supervised learning; multimodal +
Footnote †: journal: remote sensing
## 3 Methods
### Evaluation and Interpretation of the Model  
In this paper we were selected the following elements to evaluate the model's performance: the explained variance score (EVS); the mean squared error (MSE); the mean absolute error (MAE); the correlation coefficient (R); the ratio of predictions with an error within the range of \(\pm\)20% of the total predictions (a20-index); and the percentage of prediction within an error range of \(\pm
## 2 Methods
### Monte Carlo Estimation Model
#### 2.2.3 Numerical Modelling and Analysis  
The Monte Carlo model was implemented with Jupyter Python Notebooks. A total of 224 scenarios were analyzed for the aforementioned input variables and categories, with 10,000 to 40,000 simulation iterations for each scenario. Statistical summaries were calculated for \(10\cdot\log_{10}(\text{SAR}_{\text{10}_{\text{ge},\text{total}}})\) in the form of histograms, cumulative distribution functions, median, and 95 th percentiles. A logarithmic representation was chosen, as the underlying data is log-normally distributed.
# The Nuclear Receptor PXR in Chronic Liver Disease
## 6 PXR and Liver Cancer
### PXR and Hepatocellular Carcinoma
In another study, the same authors analyzed the effect of long-term PXR activation with PCN in a mouse model of liver cancer induced by DEN, observing no PCN effect on cancer progression. Nevertheless, a 22-week co-treatment with PCN and PB increased the expression of proliferation markers and the number of preneoplastic foci [94]. In contrast, a more prolonged PXR activation (35 week) attenuated CAR-mediated liver cancer promotion, by inhibiting the epithelial-mesenchymal transition (EMT) of hepatocytes. Thus, the authors suggest that the complex interplay between CAR and PXR should be further exploited to become a pharmacological target for cancer chemotherapy. Another interesting role of PXR is linked to resistance to cancer chemotherapy. Indeed, it has been demonstrated that PXR is able to contribute to chemotherapy resistance in many types of cancer, e.g., colon, breast, and prostate cancer, since its activation regulates transcription of chemoresistance-related genes, such as drug-metabolizing enzymes (e.g., CYP3A4) and transporters (P-glycoprotein and other multidrug resistance proteins) [95; 96]. Both preclinical in vitro and clinical studies showed that PXR upregulation decreased the efficacy of the anticancer drugs sorafenibb and doxorubicin by a mechanism known as TGF-\(\beta\)-induced chemoresistance in liver cancer [97]. Indeed, TGF-\(\beta\) prompts the non-canonical ERK signaling pathway, activating the transcription factor ETS1 that binds to the PXR promoter, promoting its transcription. This TGF-\(\beta\)/ERK/ETS1/PXR cascade leads to increased expression of efflux transporters and drug metabolizing enzymes, thereby inducing drug resistance [97]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Effect** & **Model** & **Reference** \\ \hline Decreased PXR gene and protein expression, IL6 & & \\ and TNF\(\alpha\) upregulation & DEN-treated mice & \\ PXR transfection reduces HepG2 migration, & PXR-transfected HepG2 cells & [91] \\ adhesion, and invasion & & \\ PXR enhances the transcriptional activity of CAR & & \\ and downregulated cells-cycle suppressor genes. & & \\ PXR activation increases hepatocytes & & \\ proliferation but does not affect cancer & Mice with DEN-induced HCC & \\ progression, whereas concomitant PXR and CAR & treated with PCN and & [94] \\ activation has a pro-carcinogenic effect & & \\ The TGF-\(\beta\)/ERK/ETS1/PXR cascade & & \\ upregulates efflux transporters and drug & & \\ metabolizing enzymes, inducing drug resistance & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Main studies investigating the involvement of PXR in liver cancer (HCC).
# Lexicon-Enhanced Attention Network Based on Text Representation for Sentiment Classification
## 3 Methodology
### Hierarchical Lexicon-Enhanced Attention Network  
When faced with large-scale text, i.e., document-level sentiment classification, we design a hierarchical classification model based on LAN. Supposing that the input document \(D=[S_{1},S_{2},\cdots,S_{D}]\) where \(D\) is the number of sentences and the \(i^{th}\) sentence \(S_{i}\) with length \(n\) is represented as \(\left[s_{1}^{i},s_{2}^{i},\cdots,s_{n}^{i}\right]\). As shown in Equation (23) in Section 3.3, we can apply LAN to encode the sentence representation from \(S\) to \(\tilde{S}\). Similarly, we can use the same LAN to encode the representations \(\left[S_{1},S_{2},\cdots,S_{D}\right]\) of all sentences in a document into \(\left[S_{1},S_{2},\cdots,S_{D}\right]\). With the sentence-level LAN to represent the sentence as the input of the proposed model, we can employ the hierarchical structure to obtain the document-level text representation for sentiment classification. Let name this model hierarchical lexicon-enhanced attention network (H-LAN). The whole hierarchical architecture of H-LAN is shown in Figure 4.  
The H-LAN model has the same architecture as the LAN model. And the only difference is that the input module of H-LAN is the sentence embedding processed by LAN instead of the lexicon-enhanced word embedding module in LAN. Specifically, the H-LAN model first uses the bidirectional LSTM network to encode the output of sentence embedding. The detailed formulas are described as follows:  
\[\overleftarrow{h_{i}^{sent}} = \overleftarrow{LSTM}\left(S_{i},\overleftarrow{h_{i-1}}\right) \tag{25}\] \[\overleftarrow{h_{i}^{sent}} = \overrightarrow{LSTM}\left(S_{i},\overleftarrow{h_{i-1}}\right) \tag{26}\]  
Figure 4: The architecture of the proposed hierarchical lexicon-enhanced attention network (H-LAN) model.  
\[h_{i}^{sent}\ =\ \left[\ \overbrace{h_{i}^{sent}\ \oplus\ \overline{h_{i}^{sent}}}^{ \right]} \tag{27}\]  
where \(h_{i}^{sent}\) is the hidden state of the \(i^{th}\) sentence \(S_{i}\) in the document. The final output of the bidirectional LSTM network is \(H^{sent}\ =\ \left[h_{1}^{sent},h_{2}^{sent},\cdots,h_{D}^{sent}\right]\).  
Following the same procedure of the multi-head attention mechanism as described in Section 3.3, we apply the multi-head attention mechanism to encode hidden states of the sentence sequence from different representation subspaces at different positions. The final document representation \(\tilde{D}\) can be computed as:  
\[\tilde{D}\ =\ \ Concat\Big{(}head_{1}^{sent},head_{2}^{sent},\cdots,head_{h}^ {sent}\Big{)}W_{O}^{sent} \tag{28}\]  
\[head_{i}^{sent}\ =\ \ Attention\Big{(}H^{sent}W_{O}^{sent},H^{sent}W_{K}^{sent },V_{D}\Big{)} \tag{29}\]  
\[V_{D}\ =\ \frac{\sum_{i=1}^{D}h_{i}^{sent}}{D} \tag{30}\]  
where \(V_{D}\) is a mean-pooling operation towards the hidden states \(H^{sent}\), which can record the overall historical sentiment information in the document.  
At last, we feed the final document representation \(D\) into a softmax classifier to obtain the probability distribution of sentiment classes.
## 2 Distributed Optical Fibre Sensors
### Rayleigh-Based Distributed Sensing
#### 2.1.2 Optical Frequency Domain Reflectometry
The problem of lack of coherence of the source--which limits the measurement range--was also tackled (e.g., by introducing phase-compensation techniques) [44]. Furthermore, the introduction of balanced photodiodes in polarisation diversity scheme determined a further improvement of the sensitivity of heterodyne detection. The resolution of some centimetres over tens of kilometres [45], or millimetres over a few kilometres [46] are now possible--at least in laboratory setups. Moreover, very recently, a commercial polarisation-sensitive OFDR (POFDR) appeared in the market, mainly for birefringence measurements and devices characterisation. To conclude this section, we report the performance of some commercial Rayleigh-based interrogators (Table 1). Please note that the table includes only devices that manufacturers specifically intended for sensing applications. Figure 2: The principle of operation of the coherent OFDR scheme.
# Online Static Security Assessment of Power Systems Based on Lasso Algorithm
## 2 Online Static Security Assessment in Power System  
In the modern energy management system (EMS), static security assessment is an important control method [1; 2; 3]. The assessment can be implemented according to the fault set. The set may contain contingencies with a single line outage, loss of generators, load variations, and so on. Line outages and load variations are considered in this paper. Then, three operating states of power systems--insecurity, alarmed, and security states--can be identified via static security assessment. The operators can take appropriate measures for each state based on the severities of faults.  
In conventional practices, security assessment is solved by repeatedly computing the power flow for all of the pre-defined outage conditions. However, the size of the fault set is generally large, and changes with the load conditions of the power system. Static security assessment will consume a large amount of time, and it is difficult to achieve in online application. Hence, the rapid and accuracy of screening and ranking contingencies is a significant issue. For this purpose, the composite security index \(PI_{c}\) is employed for evaluation, and the Lasso algorithm is applied instead of a power flow calculation to obtain the security index \(PI_{c}\) in the paper.
# Indoor Microclimate Analysis of the San Panfilo Church in Tornimparte, Italy
## 3 Results
### Continuous Monitoring
#### 3.2.4 Daily Averages  
In order to compare indoor and outdoor temperature, the daily averages have been computed during the whole campaign period (Figure 12). The measured data at the crucifix top are chosen as representative of the indoor conditions, measured data under the external portico represent the environment near the church building and the measurements at the meteorological station of Colle San Vito describe the outdoor conditions. The mean daily excursion in temperature at the meteorological station of Colle San Vito in unperturbed conditions is 10.5 \(\pm\) 3.5 \({}^{\circ}\)C, near the church it is 5.2 \(\pm\) 2.0 \({}^{\circ}\)C and inside the church it is reduced to 0.8 \(\pm\) 0.6 \({}^{\circ}\)C. Outdoor daily temperatures near the church building and at the meteorological station have the same trend, and, as expected, the temperature values measured under the portico are slightly higher than the ones at the meteorological station. Daily averaged temperature in the apex has lower fluctuations as the thermal capacity of the building is able to filter temperature variations due to meteorological events. In summer, the temperature values inside the church represent the averaged state of outdoor temperature, whereas in the cold season, it has higher values as a consequence of adsorbed heat during the summer.  
Cross-correlation between daily temperature under the portico and inside the church reveals that the maximum correlation (R = 0.97) is reached with a delay time of 3 days.  
Figure 11: **(a)** Dew point spread at crucifix top and **(b)** dew point spread below the external portico.  
Figure 12: Daily temperature inside the church (red), outside below the external portico (black) and at the S. Vito meteorological station (blue).
# Stability Analysis of the Huasushu Slope Under the Coupling of Reservoir Level Decline and Rainfall
## 3 Numerical Analysis Model
### Numerical Model  
#### 3.3.1 Two-Dimensional Numerical Model  
For the main section plane of the slope at Huasushu, GEO-SLOPE finite element software (v2024.2.1.28) was used to divide the slope into a finite element grid using quadrilateral elements. There were 5297 nodes and 5255 elements (see Figure 4 for the grid map). This two-dimensional simulation model had the advantage of being computationally efficient and suitable for preliminary slope stability analysis.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Part**} & **Volumetric** & **Deformation** & **Porosity** & **Poisson** & **Cohesion** & **Internal** & **Saturated** \\  & **Weight** & **Modulus** & **(-)** & **Ratio (-)** & **(kPa)** & **Friction** & **Permeability** \\  & **(kN/m3)** & **(MPa)** & **(-)** & **(-)** & **(-)** & **Angle (\({}^{\circ}\))** & **Coefficient (cm/s)** \\ \hline Slope mass & 20.0 & 18 & 0.36 & 0.25 & 70 & 20 & \(1.1\times 10^{-2}\) \\ Sliding belt & 19.2 & 12 & 0.21 & 0.28 & 30 & 12 & \(9.3\times 10^{-6}\) \\ Landslide & 24.0 & 17,000 & 0.16 & 0.15 & 1000 & 30 & \(1.4\times 10^{-6}\) \\ bed & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical and mechanical parameters adopted for the finite element model of the Huasushu slope.  
Figure 3: Schematic diagram of boundary conditions for Huasushu slope. v: penetration speed, f: runoff generation.
## 6 Industrial Efforts to Blockchain-Based Supply Chain Management: Tools
### Efforts against Communication Attacks
#### 6.1.1 Product Provenance and Traceability
We know that because of the use of blockchain, the platform supports product provenance to reduce fraud detection. But, it works under the assumption that the authentication of the services mechanisms are ISO27001-compliant, which unfortunately does not provide any objective standards that are automatically machine checkable when something goes wrong. Hence, we can say, it is unable to detect frauds automatically arising from subjective compliance of the authentication mechanisms. Besides, an aribus developed a blockchain-based system [69] to track goods that will eventually become a complement to (not a wholesale replacement of) suppliers' procurement software. Analogously, Air France also started to plan introducing the blockchain-based solutions to improve aircraft maintenance-operations. Also, an American flight control systems producer 'Moog' is working on a blockchain-based platform to track aircraft parts, created by 3-D printers. In another project, HMM [70] plans to improve their service and its quality by adopting high-end IT technologies, such as blockchain and IoT, in shipping and logistics. This will help them to become one of the pioneers in fully traceable and transparent shipping and logistics services. Although, all of the above-mentioned projects are initiatives and the results have to come, therefore, it is difficult to judge their effectiveness.
# Combating Web Tracking: Analyzing Web Tracking Technologies for User Privacy
## 3 Countermeasures against Web Tracking
### Tracking Measurement
#### 3.1.1 Tracking Measurements in Mobile Devices  
The user environment for the Web is rapidly shifting from desktop to mobile devices. For example, in 2011, mobile traffic accounted for only 6%, but in 2021, 56% of traffic was being transmitted from mobile devices [46]. Unlike desktops, mobile devices have various sensors such as motion and ambient sensors, and webpages use Web API to control the sensors to create the same effect as using an application. Therefore, we present a study on the use of fingerprinting techniques that utilize Web API to access sensors, and a large-scale study of Web measurement in the mobile environment.  
[PERSON] et al. [47] suggested that trackers could access a sensor when a user entered a key or PIN and could perform stateless tracking based on the sensor. Accordingly, researchers discovered that using OpenWPM-Mobile, 3695 out of 100,000 websites accessed one or more sensor APIs. Furthermore, 63% of scripts accessing motion sensors used canvas fingerprinting.  
[PERSON] et al. [15] developed OmniCrawl to propose a method to perform a comprehensive analysis of Web tracking on mobile, desktop, and privacy-focused browsers. Previous Web measurement tools developed in prior studies did not account for the mobile environment, instead relying on a PC browser emulated to appear as a mobile version (through changes in user-agent and screen resolution elements in desktop Firefox). This study utilized actual devices to gather scripts and compared the API access and features of commercially available browsers.  
[PERSON] et al. [48] proposed sensor calibration fingerprinting. This method infers the calibration value that the smartphone manufacturer takes for the correct value of the sensor. This is a highly efficient method because it can be used without the user's permission on a website or in an app. Researchers explained that the problem could be solved by rounding or adding subtle noise to the sensor value that did not affect the quality.
## 3 Results and Discussion
### Complex Ecospatial Networks Evaluation  
#### 3.2.1. Ecospatial Network Structure Analysis  
In this paper, we analyzed the ecological network structure in combination with the land use types and annual precipitation in Inner Mongolia, and the results are shown in Figure 6.  
Figure 5: Ecological nodes and ecological corridors in the study area.  
Figure 6: (**a**) Ecospatial network and land cover types in the study area; (**b**) ecospatial network and precipitation division.  
Firstly, the ecosspatial network structure was analyzed in combination with land use types. As shown in Figure 5(a), the study area is mainly composed of woodland, grassland, cultivated land and unused lands, such as sandy land, Gobi, and saline-alkali land, while water and residential land are relatively small. Overall, the network structures such as woodlands in the north and sandy lands in the west are relatively simple. The ecological nodes in the north are mostly distributed near the forest-grassland ecotone and the farm-forest ecotone; in the west, due to habitat fragmentation, land desertification and a series of impacts caused by them, there are fewer ecological nodes, which are partly gathered in small grassland patches and partly scattered and far apart with the fragmented grassland patches. The stability of the ecos spatial network structure in these areas is relatively low. The distribution of ecological nodes in these areas is relatively dense and uniform, with interlocking ecological corridors and better network integrity and connectivity.  
As shown in Figure 5(b), affected by monsoon circulation and topography, the overall annual precipitation in Inner Mongolia gradually decreases from east to west, and the complexity of the ecosspatial network structure first increases and then decreases from east to west. The areas with annual precipitation between 22 and 158 in the west and between 667 and 801 in the northeast are the areas with the least and most annual precipitation, respectively, and the ecos spatial networks in these two areas are relatively sparse, with few and scattered nodes. The areas with annual precipitation between 392 and 666 are rainy areas, and the ecos spatial network is aggregated in a small scale; in the areas with annual precipitation between 159 and 391, the ecos spatial network is aggregated in a large scale, with numerous and dense ecological nodes and corridors and strong network stability. Overall, the correlation between the ecos spatial network structure and annual precipitation is not strong, so the subsequent study will not combine with annual precipitation anymore.