esa-sceva/satcom-chunk-collection huggingface dataset:
# Goal Oriented Time Adaptivity Using Local Error Estimates
## 3 Time Adaptivity Based on Local Error Estimates
### Error Estimation and Timestep Controller  
We now derive an estimate for the local error using two solutions of different order. We approximate the local error behavior by a simplified model, focusing on the leading terms. Aiming to keep the norm of the local error equal to a desired tolerance, this becomes a timestep controller yielding \(\Delta t_{n+1}\) based on timestep \(\Delta t_{n}\), the local error estimate and a tolerance \(\tau\).  
Assume two schemes \(\mathcal{N}^{t,\Delta t}\), \(\mathcal{N}^{t,\Delta t}_{-}\) with orders \(p\), \(\hat{p}\) and corresponding principal error functions \(\mathbf{\phi}\), \(\mathbf{\phi}_{-}\). We estimate  
\[\mathbf{\ell}_{n}:=\left(\mathcal{N}^{t_{n},\Delta t_{n}}_{-}-\mathcal{M}^{t_{n}, \Delta t_{n}}\right)\mathbf{u}_{n}\quad\text{by}\quad\hat{\mathbf{\ell}}_{n}:=\left( \mathcal{N}^{t_{n},\Delta t_{n}}_{-}-\mathcal{N}^{t_{n},\Delta t_{n}}_{+} \right)\mathbf{u}_{n}. \tag{8}\]  
This error estimate is _asymptotically correct_, i.e., the leading term of \(\hat{\mathbf{\ell}}_{n}\), characterized by the principal error function \(\mathbf{\phi}_{-}\), matches the leading term of \(\mathbf{\ell}_{n}\). We model the local error \(\mathbf{\ell}_{n}\) via  
\[\mathbf{m}_{n}:=\Delta t_{n}^{\hat{p}+1}\mathbf{\phi}_{-}(t_{n},\mathbf{u}_{n}). \tag{9}\]  
Assuming \(\mathbf{\phi}_{-}(t_{n},\mathbf{u}_{n})\) to be slowly changing, the next step of this model yields  
\[\mathbf{m}_{n+1}\approx\Delta t_{n+1}^{\hat{p}+1}\mathbf{\phi}_{-}(t_{n},\mathbf{u}_{n})= \left(\frac{\Delta t_{n+1}}{\Delta t_{n}}\right)^{\hat{p}+1}\mathbf{m}_{n}\approx \left(\frac{\Delta t_{n+1}}{\Delta t_{n}}\right)^{\hat{p}+1}\hat{\mathbf{\ell}}_{n}.\]  
Aiming for \(\|\mathbf{m}_{n+1}\|=\tau\) gives the well-known deadbeat controller  
\[\Delta t_{n+1}=\Delta t_{n}\left(\tau/\|\hat{\mathbf{\ell}}_{n}\|\right)^{1/(\hat{ p}+1)}. \tag{10}\]  
This controller is based on asymptotically correct estimates of the local error for \(\mathcal{N}^{t,\Delta t}_{-}\), we propagate the solution using \(\mathcal{N}^{t,\Delta t}_{+}\) for its higher order. This is known as _local extrapolation_[23].
# In Search of Lightweight Deployable Tensegrity Columns
## 5 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Column Name** & **Simplex** & **Overlapped Simplex** & **X** & **Octahedron** \\
**Deployment Scheme** & **1 st/2 nd** & **1 st/2 nd** & **1 st/2 nd/3 rd** & **1 st/2 nd** \\ \hline
**Parameter** & & & & \\ \hline Number of struts & 12 & 12 & 8 & 16 \\ Number of cables & [27] /37 & 54/55 & 36 & 56 \\ Number of plants & [12] /18 & 24/25 & 18 & 34 \\ Number of supports & [3] & [3] & 4 & 4 \\ \hline Volume of the package (m\({}^{3}\)) & \(1.7\)/\(\left[\begin{array}{c}0.05\text{(bundred)}\\ \end{array}\right]\) & \(2.0\)/\(\left[\begin{array}{c}0.05\text{(bundred)}\\ \end{array}\right]\) & \(0.9\)/\(0.5\)/\(0.9\) & \(0.9\)/\(1.7\) \\ Compression ratio (\%) & \(40\)/\(\left[\begin{array}{c}1\\ \end{array}\right]\) & \(50\)/\(\left[\begin{array}{c}1\\ \end{array}\right]\) & \(20\)/\(10\)/\(20\) & 40 \\ Space for deployment (m\({}^{2}\)) & \(2/4\) & \(2.3\)/\(4\) & \(2/4\)/\(2\) & \(1.4\)/\(2\) \\ \hline Column layout & modular & non-modular & modular & extra cables \\ Package type & plane/bundle & plane/bundle & plane/plane/plane & plane \\ Deployable with hands & no/yes & no/yes & no/yes & no/yes & no/no \\ Deployable by one person & yes/yes & yes/yes & yes/yes & yes/yes \\ Sensitivity to external loads & no/moderate & no/moderate & no/moderate/moderate & no/yes \\ Number of active cables & 27/8 & 54/33 & \(36\)/\(\left[\begin{array}{c}7\\ \end{array}\right]\)/\(\left[\begin{array}{c}2\\ \end{array}\right]\) & \(56\)/\(16\) \\ Number of actuators & \(16/8\) & \(23/21\) & \(16\)/\(\left[\begin{array}{c}4\\ \end{array}\right]\)/\(16\) & \(28\)/\(8\) \\ \hline Tensegrity class & 2 & 1 & 1 & 2 \\ Number of infinitesimal mech. & 4 & 1 & 1 & 8 \\ Number of self-stress states & 4 & 1 & 1 & 4 \\ \hline Mass (kg) & 71 & 89 & [64] & 88 \\ Density of the structure (bg/m\({}^{3}\)) & 17.75 & 22.25 & [16.00] & 22.00 \\ Dimensions LCC & 0.084 & 0.049 & 0.066 & [0.119] \\ Load carrying capacity (kN) & 58.2 (strut failure) & 43.1 (strut failure) & 41.7 (strut failure) & [102.7] & [0.085 of equilibrium) \\ Pre-extension (mm) & 34.5 & 76.4 & 81.4 & 2.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the columns parameters. Apart from the results presented in Table 6, two additional parameters were considered in the analysis: maximum forces in structural members (Figure 7) and deflection of the columns (Figure 8). Figures 7 and 8 show how these parameters depend on the self-stress level \(S_{0}\). It can be noticed that the octahedron column is very stiff as its deflection is almost constant within the considered range of prestressing forces. The least stiff is the X column; however, the simplex column is more prone to structural control than the X as its deflection decreases most rapidly with an increase of \(S_{0}\). The biggest increase of maximum tension forces in cables and the decrease of the minimum compression forces in struts are observed in the octahedron column, whereas the X and overlapped simplex columns are the least sensitive to self-stress in this regard.
## 4 Experiment and Analysis
### Impacts of Initial Guess  
The measurement error \(\sigma\) = 0.0 \({}^{\circ}\)C, and the measurement point was taken as \(M\) = 41. The initial guesses were set as 0.1, 0.2, and 0.3 for cases 1 and 2. The inversion results of the different initial guesses are shown in Figures 4 and 5, and the average relative error is shown in Table 1.  
Table 1 and Figures 4 and 5 show that the identification results were insensitive to the initial guesses of the unknown boundaries.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Initial Guess (m)** & **Case 1 Average Relative Error (\%)** & **Case 2 Average Relative Error (\%)** \\ \hline
0.1 & 0.92 & 0.95 \\
0.2 & 0.95 & 1.04 \\
0.3 & 0.89 & 1.63 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Impacts of initial guess of cases 1 and 2.  
Figure 4: Identification results of different initial guess boundaries of case 1.  
Figure 5: Identification results of different initial guess boundaries of case 2.
## 4 Backgrounds and Methods
### Handover Procedure of the Dual MRNs
#### 4.3.1 mMRN Handover Procedures  
In this subsection, we outline the HO procedure of mMRN. Figure 10 depicts the procedure used in the proposed HO scheme. The phases comprising the HO procedure are outlined below.  
Figure 9: Proposed network model with dual MRNs. The HO procedure of mMRN is completed.  
1. Decision phase: This phase begins when the serving DeNB receives a measurement report (M report) from the mMRN. On receiving the report, the serving DeNB makes the HO decisions using Event A3 and calculates the HO parameters using the proposed algorithm presented in Section 3.1. If the serving DeNB decides that the mMRN must perform the HO, then it sends an HO request (HO req) to the target DeNB. The HO request sent contains the HO parameters and context information for the target DeNB to prepare the resource allocation. After preparing the radio resource, the target DeNB accepts all MRNs and replies with an HO request ACK (HO req ACK) containing parameters for the serving DeNB and the HO procedure. Finally, the serving DeNB receives an HO request ACK and sends the HO command and radio resource control (RRC) connection reconfiguration (HO command, RRC conn) to the mMRN.
2. Synchronization phase: In this phase, the DeNB stops forwarding data to the mMRN; it does not serve data to the UEs inside of the vehicle during the HO procedure. Until the HO procedure of  
Figure 10: Flowchart of the proposed HO scheme for dual MRNs.  
mMRN has been completed, only the sMRN serves the UEs. On completion, the mMRN tries to synchronize with the target DeNB by sending a synchronization command.
3. Tracking area update phase: On successfully synchronizing with the target DeNB, the mMRN sends a tracking area update command to the MME, which the MME uses to update the mMRN's location.
4. Path switch phase: The target DeNB switches the routing path to the mMRN by sending a path switch update request (path switch req) command to the MME. On receiving this request, the MME issues a user plane request to the S-GW to switch the routing path. On completing the path switch, the S-GW replies with a user plane update message to the MME, which, in turn, sends a path switch request ACK message to the target DeNB.
5. Final phase: In the final phase, the mMRN updates its S1/X2 configuration. Then, the target DeNB and S-GW begin forwarding data along the new path to the mMRN. At this point, the HO procedure is complete.
## 3 Construction 4.0. BIM and Information Technologies
### BIM and GIS  
GIS is an established technology that has been widely developed and used for city and infrastructure planning. Traditionally, it has been used for territorial analysis. However, public administrations use it currently for the publication of their information, whether geographical, essential for the planning of linear infrastructures, or of diverse non-geospatial typology such as pollution data or traffic levels.  
The main research in this area focuses on the integration of GIS information with the three-dimensional models used in BIM. GIS information is territorial in scope and optimised for storage, whereas BIM modelling uses a smaller scale. This integration is complex and limited due to the files format, although planning software such as ArcGIS or InfraWorks permit linking the two systems [81]. This is due to open formats such as CityGML and IFC, respectively [74].  
The availability of a big data volume has enabled the development of the smart cities concept over the last few years. A BIM-GIS environment provides a suitable environment for sustainable, reliable, and energetically efficient smart city construction [82]. The two technologies bring complementary information, GIS at the territorial scale (macro) and BIM at the individual scale (micro) [83], helping the decision-making process for managers [84].  
Beyond smart cities, studies have focussed on completing the BIM model with the geospatial information of its environment provided by GIS, whether it be in highways [85], airports [86], sewage systems [87], hydroelectric projects [88] or railways [61].
# Design of Wireless Sensor Network for Agricultural Greenhouse Based on Improved Zigbee Protocol
###### Abstract  
Greenhouse cultivation technology has greatly contributed to the development of agriculture in Malaysia. Understanding how to monitor the greenhouse environment with high efficiency and low power consumption is particularly important. In this research, a wireless sensor network for agricultural greenhouses based on the improved Zigbee protocol is designed. Its hardware consists of various sensors and Zigbee nodes commonly used in agricultural greenhouses. On the basis of this hardware, this research designed the network topology of WMN (Wireless mesh network) by comparing the advantages and disadvantages of various topologies, and combined with this structure, proposed an improved Zigbee routing protocol EMP-ZBR to solve the question regarding energy loss and the network congestion of wireless networks. After testing EMP-ZBR and traditional Zigbee routing protocols, the improved EMP-ZBR protocol is superior to traditional Zigbee routing in terms of the end-to-end average delay, packet delivery rate, routing control overhead and routing discovery frequency, which were optimized about 1.1%, 15.2%, 15.2%, 8.1 ms in different mobile pause times, and 9.8%, 19.3%, 15.7% and 121 ms in different packet sending rates. The agreement proves that EMP-ZBR can more effectively alleviate the impact of congestion and improve the overall performance of the data monitoring system for agricultural greenhouses.  
agricultural sensor network; EMP-ZBR; network topology; routing transmission algorithm 2023135188 [[https://doi.org/10.3390/agriculture13081518](https://doi.org/10.3390/agriculture13081518)]([https://doi.org/10.3390/agriculture13081518](https://doi.org/10.3390/agriculture13081518))  
1
# Zero-Shot Detection of Visual Food Safety Hazards via Knowledge-Enhanced Feature Synthesis
## 2 Related Work
### Food Safety Inspection and Visual Analysis  
Visual assessment has long been a cornerstone of food safety inspection, traditionally performed by human inspectors trained to recognize signs of contamination, spoilage, or foreign objects [13]. Recent advances in computer vision have enabled more automated approaches to food safety inspection, reducing reliance on subjective human judgment and increasing throughput in food production environments [14].  
Early computer vision systems for food safety focused on detecting specific contaminants using handcrafted features and traditional machine learning techniques [15]. For example, [PERSON] et al. [16] developed methods for foreign object detection in food products using color and textural features, while [PERSON] et al. [17] employed spectral imaging techniques to detect surface contaminants on poultry products.  
The emergence of deep learning has significantly advanced automated food safety inspection. Convolutional Neural Networks (CNNs) have become the dominant approach for detecting various safety hazards, including mold [18], foreign objects [19], and microbial contamination [4]. For instance, [PERSON] et al. [20] employed CNNs to detect fruit diseases and defects in agricultural settings, while [PERSON] et al. [6] developed a multi-level deep learning system for the rapid detection of various food hazards.  
Despite these advances, most current deep learning approaches for food safety inspection operate in a closed-set paradigm, where all hazard types must be present in the training data. This limitation significantly impairs their ability to detect novel or rare safety issues not represented during training--a critical shortcoming in real-world scenarios where new types of contamination constantly emerge [21]. Our work addresses this limitation by developing a zero-shot detection framework specifically tailored for food safety hazards.
# Near Real-Time Irrigation Detection at Plot Scale Using Sentinel-1 Data
###### Abstract  
In the context of monitoring and assessment of water consumption in the agricultural sector, the objective of this study is to build an operational approach capable of detecting irrigation events at plot scale in a near real-time scenario using Sentinel-1 (S1) data. The proposed approach is a decision tree-based method relying on the change detection in the S1 backscattering coefficients at plot scale. First, the behavior of the S1 backscattering coefficients following irrigation events has been analyzed at plot scale over three study sites located in Montpellier (southeast France), Tarbes (southwest France), and Catalonia (northeast Spain). To eliminate the uncertainty between rainfall and irrigation, the S1 synthetic aperture radar (SAR) signal and the soil moisture estimations at grid scale (10 km \(\times\) 10 km) have been used. Then, a tree-like approach has been constructed to detect irrigation events at each S1 date considering additional filters to reduce ambiguities due to vegetation development linked to the growth cycle of different crops types as well as the soil surface roughness. To enhance the detection of irrigation events, a filter using the normalized differential vegetation index (NDVI) obtained from Sentinel-2 optical images has been proposed. Over the three study sites, the proposed method was applied on all possible S1 acquisitions in ascending and descending modes. The results show that 84.8% of the irrigation events occurring over agricultural plots in Montpellier have been correctly detected using the proposed method. Over the Catalonian site, the use of the ascending and descending SAR acquisition modes shows that 90.2% of the non-irrigated plots encountered no detected irrigation events whereas 72.4% of the irrigated plots had one and more detected irrigation events. Results over Catalonia also show that the proposed method allows the discrimination between irrigated and non-irrigated plots with an overall accuracy of 85.9%. In Tarbes, the analysis shows that irrigation events could still be detected even in the presence of abundant rainfall events during the summer season where two and more irrigation events have been detected for 90% of the irrigated plots. The novelty of the proposed method resides in building an effective unsupervised tool for near real-time detection of irrigation events at plot scale independent of the studied geographical context.  
Keywords:irrigation; plot scale; near real-time; Sentinel-1 +
Footnote †: journal: Remote Sensing
# A Comparative Analysis of Anomaly Detection Methods in IoT Networks: An Experimental Study
## 3 Methodology  
### Datasets in IoT  
The analysis performed, described in detail in [33], included a review of 44 IoT and industrial IoT security datasets published between 2018 and 2022. The aim of the study was to create a comprehensive compilation of resources that could be used by researchers to evaluate advanced ML techniques and design effective IoT security systems. The collected datasets show considerable diversity, but several common features can be identified. Most of them are publicly available or require only notification, which makes it easy for researchers to access the data. The dominant file formats are CSV and PCAP, which ensures broad compatibility with data analysis tools. It is also worth noting that significant parts of the datasets were generated in simulated environments, which allowed for controlled experiments. Based on the analysis, two datasets were selected that stood out from the rest and were particularly useful for IoT security research.
## 3 Materials and Methods  
### Chemicals and Reagents  
The analytes 3,4-dihydroxypropylglyglykol (DHPG), 3-methoxy-4-hydroxyphenylglyglycol (MHPG), and vanililmandelic acid (VMA) and the reagents acetone, acetonitrile (ACN), chlorophyll, dichloromethane (DCM), boric acid, formic acid (FA), sodium tetraborate, sodium hydroxide (NaOH), sodium hydroxycarbonate, sodium dodecyl sulphate (SDS), and 1-hexyl-3-methylimidazolium chloride were purchased from Merck (Darmstadt, Germany). The reagents ammonia, ammonium acetate, diethyl ether, and ethanol (96%) were obtained from POCH (Gliwice, Poland). Methanol (MeOH), n-hexan, and propan-2-ol (IPA) were supplied by VWR International Poland (Gdansk, Poland). All chemicals were of analytical grade and were applied without further purification. The Capillary Regenerator Basic Wash (0.1 M sodium hydroxide) was purchased from Beckman Coulter (Brea, CA, USA). Different types of SPE cartridges such as: Evolute(r) Express ABN, 30 mg/1 mL; Evolute(r) Express AX 30 mg/1 mL; and Evolute(r) Express CX, 30 mg/1 mL were purchased from Biotage (Uppsala, Sweden); cartridges LiChrolut(r) C18 and CN were purchased from Merck (Darmstadt, Germany); DVB cartridge 30 mg/1 mL was supplied by UCT (Bristol, PA, USA); and the Supel(tm)-Select HLB, 30 mg/1 mL and the Styrene-DVB (Styre screen) sorbent were purchased from Supelco/Sigma-Aldrich (St. Louis, MO, USA). The deionized (DI) water (18.2 M\(\Omega\) cm) used in all experiments was obtained from Milli-Q apparatus from MiliporeSigma (Burlington, MA, USA). The 3D-printed DSPE system consisting of four printed elements was fabricated in Department of Pharmaceutical Chemistry, Medical University of Gdansk, Poland. Artificial urine (SAE0074-50 ML) was purchased from Sigma-Aldrich (St. Louis, MO, USA).  
Figure 2: Electrophrogram of standard solution of DHPG, VMA, and MHPG at a concentration of 10 \(\mathrm{\SIUnitSymbolMicro g}\)/mL. Separation conditions: applied voltage 25 kV; capillary total length 50.2 cm and 75 \(\mathrm{\SIUnitSymbolMicro m}\) i.d.; \(\lambda\) = 200 nm; injection time 30 s (at 0.5 psi); analysis time 8 min. The BGE consisted of 5 mM sodium tetraborate decahydrate, 50 mM SDS, 15% (\(v/v\)) MeOH, 150 mM boric acid, and 1 mM of 1-hexyl-3-methylimidazolium chloride (the apparent pH of the BGE equaled 7.3).
## 4 Results and Analysis  
Figure 9 presents the results of the \(S/N\) ratio for the five parameters at three levels. According to Figure 9, the optimal parameters for a higher material removal rate were obtained at 750 RPM (level 3), 10 mm/s feed rate (level 1), 30 N force (level 3), 90 Shore A hardness (level 3), and 60 grit (level 1). The Material Removal Rate (MRR) increases with increasing Rotation per Minute (RPM), force, and hardness. On the contrary, it increases with decreasing feed and grit size. With coarser grain, i.e., smaller grit size, the depth of cut increases. An increase in force imparted on the work coupon and hardness of contact wheel results in an increase of material removal. The increase of RPM in the contact wheel causes greater tangential force, thereby causing the enhancement of the depth of cut. The decrease in feed rate causes the contact time between the grains and work coupon to be maximized, resulting in a higher material removal rate.  
Figure 8: Standard deviation of the depth of cut taken from Taguchi based L27 orthogonal experimental trials.  
Figure 9: Mean signal-to-noise (\(S/N\)) ratio graph for depth of cut.
# SMM: Leveraging Metadata for Contextually Salient Multi-Variate Motif Discovery
### Key Questions  
In this paper, we note that, in addition to relying on different data structures and algorithms to seek motifs, existing algorithms make very constraining assumptions about what constitutes a re-occurring multi-variate pattern. While most authors agree that a pattern does not need to be exactly repeated and some variations can be allowed, several questions remain:  
* Do the instances of a recurring pattern need to cover the same sub-set of variates (Figure 1, diribbling action), or can they occur on a different subsets of variates (Figure 2, climbing action)?
* Can the re-occurring pattern stretch or shrink in time, due to variations in speed, or does it have to be, more or less, the same length as the original?
* Is there a lower bound on the number of times a pattern repeats, before being marked as a motif, or is repeating once sufficient?
* Does any re-occurring pattern count as a motif, or do these patterns have to satisfy some additional constraints, such as being concentrated on a set of variates which are contextually related (such as sensors on the same arm)?  
Most existing algorithms provide only answers to a subset of these questions; the repeated pattern is assumed to occur on the same set of variates, the duration of the repeated instances is constrained to be more or less identical and any pair of repeating patterns is returned as a motif, irrespective of whether the variates have any contextually relevant relationships.
# Performance Evaluation of MPTCP on Simultaneous Use of 5G and 4G Networks
## 2 An Overview of The MPTCP Schedulers and Congestion Control Algorithms
### MPTCP Schedulers
#### 2.1.2 Round-Robin  
In the Round-robin scheduler, the SF for sending a packet is selected in a Round-robin fashion [28]. It circularly picks up an SF among all the SFs having available CWND.
## 4 Derivation of Energy Efficiency Expression  
This section introduces our main analysis model and derives the closed-form expressions of the average achievable downlink rate, the network power consumption and the network EE, respectively.  
Figure 3: The user association probability of unprotected subframes (USF) micro-cell user equipments (MUEs) and protected subframes (PSF) MUEs versus \(\rho\) with fixed \(\lambda_{p}=3\lambda_{m}\) and fixed \(\lambda_{u}=0.0018\).