esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Treatments and Experimental Setup  
The field study involved an initial fallow period followed by crop production for the next three years. A detailed description and flow chart of the experiment is provided in Figure 2. The total area of the field was 26 m\({}^{2}\) and was divided into 1.5 \(\times\) 1.5 m\({}^{2}\) plots. Each plot was separated from other plots using 60 cm high and 20 cm wide ridges. Five treatments were included in the experiment: the control with no applications, treatment with a 100% soil gypsum requirement (SGR) (G100), and 3 other treatments receiving 50% SGR (5 t ha\({}^{-1}\)) combined with 12 t ha\({}^{-1}\) farmyard manure (FYM + G50), poultry manure (PM + G50) and green manure (GM + G50). The properties of organic amendments are presented in Supplementary Table S1. The amendments were applied evenly and mixed into the soil manually before the experiment started, and then the plots were covered and left to fallow. The purpose of the fallow period was to monitor the impact of the selected treatments on the salt-affected soil area without complicating the impact of the growing crop. This was performed over the first 45 days. During the fallow period, all of the subplots were regularly irrigated to maintain the moisture levels of the soil at field capacity. The meteorological variation during this experiment is presented in Supplementary Figure S1. Soil samples were taken using random sampling by auger to a depth of 30 cm. Randomly positioned soil samples were collected and homogenized to make a composite sample before and after the 45-day fallow period.  
Figure 1: Study area.  
Figure 2: Overview of the study. Note: SOC = soil organic carbon.
# Few-Shot Continuous Authentication for Mobile-Based Biometrics
## 4 Experiments
### Dataset  
Two publicly available datasets were used for our experiments. We utilized three types of sensor data, i.e., touch screen, accelerometer, and gyroscope data, while users touch a smartphone screen.  
* The HMOG dataset [37] is a multimodal dataset containing touch screen, accelerometer, and gyroscope data captured from 100 subjects while doing three tasks, e.g., reading web articles, typing texts, and navigating a map. The dataset comprises 24 sessions (8 sessions for each task) for each subject. The data are captured under sitting and walking situations. We used data of these tasks under sitting situations.
* The BBMAS dataset [38] captures touch screen, accelerometer, and gyroscope data from 117 subjects while typing free and fixed texts. These data are captured under sitting and walking situations. We used only data under the sitting situations. We removed five subjects' data because of too small a data size, resulting in 112 subjects' data.  
In both datasets, we obtained data samples by a sliding window with a window size \(T_{w}\) of 1 s (100 sensor readings). The overlapping of the window \(T_{o}\) was set as 0.5 s (50 readings) in the HMOG dataset and 0.9 s (90 readings) in the BBMAS dataset. We obtained an average of 1672 and 862 samples per user in the HMOG and BBMAS datasets, respectively.
###### Abstract  
This study investigates highway pavement subsidence along Road 431, Israel, using an integrated geophysical framework that combines Interferometric Synthetic Aperture Radar (InSAR), Ground Penetrating Radar (GPR), and Electrical Resistivity Tomography (ERT). These methods address the limitations of standalone techniques by correlating surface subsidence patterns with subsurface anomalies. InSAR identified surface subsidence rates of up to \(-2.7\) cm/year, pinpointing subsidence hotspots, while GPR detected disintegrated fill layers and air voids, and ERT revealed resistivity anomalies at depths of 50-100 m linked to karstic cavities and water infiltration. Validation through borehole drilling confirmed structural heterogeneity, specifically identifying karstic voids in limestone layers and weathered chalk layers that align with the geophysical findings. The findings highlight the complex interplay of geological and hydrological processes driving ground instability, exacerbated by groundwater fluctuations. This study demonstrates the novelty of combining surface and subsurface monitoring methods, offering a detailed diagnostic framework for understanding and mitigating geotechnical risks in transportation infrastructure.  
InSAR; GPR; ERT; highway safety; subsidence monitoring; karst characterization; multidisciplinary geophysical approach Article  
1  
Academic Editors: [PERSON] and [PERSON]  
Received: 15 January 2025  
Revised: 1 February 2025  
Accepted: 7 February 2025  
Published: 9 February 2025  
**Citation**: [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] An Integrated Study of Highway Pavement Subsidence Using Ground-Based Geophysical and Satellite Methods. _Appl. Sci._**2025**, _15_, _1788_. [[https://doi.org/10.3390/app15041758](https://doi.org/10.3390/app15041758)]([https://doi.org/10.3390/app15041758](https://doi.org/10.3390/app15041758))  
**Copyright**: (c) 2025 by the authors.  
License MDPI, Basel, Switzerland.  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license  
([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Survey on Security Threats in Agricultural IoT and Smart Farming
## 3 Security Threats in Modern Agriculture
### Cybercrime and Cybersecurity in Agriculture
there are five main factors that strengthen these risks in the agriculture sector: (i) the increasing farm consolidation and the associated heavy reliance on technology; (ii) the vertical integration across the food supply chains, where producers may directly trade products and execute processes; (iii) the absence of compliance with food safety, traceability and insurance requirements and regulations; (iv) the increasing dependence of components among food-related systems in smart markets, which leads to greater exposure to failures and faults and (v) the lack of systematic surveillance of food-related systems, social media and markets in a secured, dynamic and near real-time manner, in order to detect significant digital and security issues, which might be the cause of important information breaches and system flaws [87]. This increased risk of cyber-attacks can lead to various ways through which these attacks can impact the agricultural domain. [PERSON] et al. in [84] classify these ways as: (i) disruption of delivery; (ii) interception of confidential information; (iii) alteration of formulations; and (iv) tampering threats. Obviously, these threats are strongly attached to the adoption and the usage of technological advances in agriculture and food industries. [PERSON] et al. as well as [PERSON] et al. recognize the main technological advancement areas which are widely adopted in the agri-sector and consist susceptible points for malicious cyber-attacks as follows [86; 88]:* Wireless connections (e.g., Wi-Fi);
* Radio Frequency Identification (RFID);
* Air, soil and crop and infrastructure sensors;
* Unmanned Aerial Vehicles (UAVs), e.g., drones;
* Automation systems focused on PA (e.g., Real Time Kinematic Technology);
* Mobile devices (e.g., laptops, mobile phones, GPS trackers);
* Vertical farming and smart agriculture;
* The combination of biotechnology and nanotechnology with Artificial Intelligence (AI). The agricultural sector is mainly interwoven with people's nutrition so every risk, vulnerability and threat represents a major risk for public health. Cybersecurity is, in general, an open and evolving research field and when it comes to the agricultural domain and its application, its significance is of utmost importance. The survey conducted for cybersecurity in agriculture and its threats can be summarized in Table 2.
# Equilibrium Inefficiency and Computation in Cost-Sharing Games in Real-Time Scheduling Systems +
## 3 Monomial Cost Functions
### Decreasing Cost Shares: \(d<1\)
Lemma 2: _For every positive integer \(h\) and for every cost function \(c(x)=x^{d}\) with \(d<1\), there exists a game with \(n=h^{2}+h\) jobs, and a NE \(s\) of that game, such that:_  
\[\frac{C(s)}{C(s^{*})}=\Omega\Big{(}h^{1-d}\Big{)}=\Omega\Big{(}n^{(1-d)/2} \Big{)},\]  
_where \(s^{*}\) is an optimal assignment of that game._  
Proof: Our instance has \(n=h^{2}+h\) jobs and \(2h+1\) slots. For ease of exposition, we shift time and call the slots \(-h,-h+1,\ldots,-1,0,1,\ldots,h-1,h\). There exist \(j\) jobs that can use slots \([-j,0]\) and \(j\) jobs that can use slots \([0,j]\) for \(j=1,2,\ldots,h\). Observe that slot \(0\) is the only one that is common to all intervals. The optimal solution \(s^{*}\) would place all jobs on \(0\) for a total cost:  
\[C(s^{*})=(h^{2}+h)^{d}=\Theta(n^{d}).\]  
Consider the following assignment \(s\), which we argue is a NE. Every one of the \(j\) jobs with interval \([-j,0]\) selects slot \(-j\) and every one of the \(j\) jobs with interval \([0,j]\) selects slot \(j\). Note that the number of jobs on slot \(t\) is \(|t|\). This fact implies the assignment is actually a NE, since the jobs on any slot \(t\) share the slot with \(|t|-1\) other jobs, while every other slot \(t^{\prime}\) between \(t\) and \(0\) has \(|t^{\prime}|\leq|t|-1\) jobs. The cost of this assignment is:  
\[C(s)=\sum_{j=1}^{h}2j^{d}\geq 2\int_{1}^{h}x^{d}dx=\frac{2}{d+1}\Big{(}h^{d+1 }-1\Big{)}=\Theta\Big{(}n^{(d+1)/2}\Big{)}.\]  
Taking the ratio \(C(s)/C(s^{*})\) completes the proof. Remark 1: _From the proofs of Lemmas 1 and 2, it follows that the worst case PoA is in fact approximately:_  
\[\sum_{j=1}^{h_{t}}2c(j),\]0
# Performance Evaluation of NoSQL Document Databases: Couchbase, CouchDB, and MongoDB
## 2 Related Work
They also concluded that MongoDB started to reduce performance with increasing data size and that Cassandra is faster than MongoDB, providing lower execution time regardless of database size. The authors in [9] compared two NoSQL databases: HBase, which is a NoSQL column database, and MongoDB, which is a document database. The two databases were compared and evaluated using YCSB with two more workloads, G and H, that focus on update operations. The workloads D and E were removed from the experiments because they contain insert operations. The main purpose of this work was only the evaluation of read, write, and load operations. The databases were evaluated in an Ubuntu environment using 600,000 records. It was concluded that MongoDB was more efficient in read operations, and HBase performed better in write operations. In [10], three NoSQL document databases were compared: Couchbase, CouchDB, and MongoDB. The databases were characterized, taking into consideration their architecture, main advantages, and limitations. Furthermore, the authors did not perform an experimental evaluation and only used the OSSPal methodology to compare the databases. OSSpal methodology combines quantitative and qualitative measures to assess open-source software, where seven categories are defined: functionality, operational software characteristics, software technology attributes, documentation, support and service, community and adoption, and development process. In the end, a score that identifies the best NoSQL document database was obtained. In this evaluation, the best score was obtained by MongoDB, followed by Couchbase and CouchDB. In [11], two databases were compared: MySQL and MongoDB. The databases were evaluated using YCSB, but only workloads A, C, and F were used. These workloads are composed of the reading, updating, and read-modify-write operations. The number of records also varied between 5 hundred, 12 thousand, 500 thousand, 100 thousand, 150 thousand, and 200 thousand. The number of threads used also varied between 4, 8, 16, and 32. It was concluded that MongoDB has better performance than MySQL in all operations used. Unlike this author, we used 100 thousand, 1 million, and 10 million records and 1, 3, and 6 threads. Unlike most works, we used a Windows environment rather than an Ubuntu environment because, according to Statista and Stat Counter Global Stats websites, \"Microsoft Windows is the dominant desktop operating system worldwide\".
# Effect of Vinyl Flooring on the Modal Properties of a Steel Footbridge
###### Abstract  
Damping ratios associated with non-structural elements play an important role in mitigating the pedestrian-induced vibrations of slender footbridges. In particular, this paper analyses the effect of vinyl flooring on the modal parameters of steel footbridges. Motivated by the unexpected high experimental damping ratios of the first vibration modes of a real footbridge, whose deck was covered by a vinyl flooring, this paper aims at assessing more accurately the experimental damping ratios generated by this non-structural element on steel footbridges. For this purpose, a laboratory footbridge was built and vinyl flooring was installed on it. Its numerical and experimental modal parameters without and with the vinyl flooring were determined. The operational modal analysis method was used to estimate experimentally the modal parameters of the structure. The damping ratios associated with the vinyl flooring were obtained via the substraction between the experimental damping ratios of the laboratory footbridge with and without the vinyl flooring. An average increase of the damping ratios of 2.069% was observed due to the vinyl flooring installed. According to this result, this type of pavement may be a useful tool to significantly increase the damping ratios of steel footbridges in order to reduce pedestrian-induced vibrations.  
Keywords:non-structural damping; passive control; ambient vibration test; operational modal analysis; footbridges +
Footnote †: journal: Applied Mechanics and Physics
# An Ensemble-Based Machine Learning Approach for Cyber-Attacks Detection in Wireless Sensor Networks
## 5 Conclusions and Future Work  
Wireless Sensor Networks, a key underlying technology of the Internet of Things, are prone to several types of cyber-attacks that could compromise availability, privacy, control, and reliability. One of the effective methods for detecting and mitigating cyber-attacks on WSNs is Machine Learning. Classifier ensembles have been successfully used to detect cyber-attacks in various scenarios. We proposed a new ensemble-based approach, Weighted Score Selector, which uses a pool of conventional supervised ML techniques: Support Vector Machine, Gaussian Naive Bayes, Decision Tree, K-Nearest Neighbor, Random Forest, and LightGBM. The proposed approach's performance was compared to three classical ensemble techniques that integrate the decisions of multiple machine learning models to detect cyber-attacks on WSNs. Performance comparisons were conducted using the following metrics: accuracy, probability of false alarm, probability of detection, probability of misdetection, memory usage, processing time, and prediction time per sample.  
Evaluation results indicated that the proposed ensemble approach yielded promising results in terms of probability of detection, probability of false positives, and probability of misdetection. Future work could entail expanding the presented approach to examine more evaluation metrics or additional heterogeneous classifiers using other datasets. This work can be accomplished by extending the pool of base classifiers to include other machine learning algorithms, such as unsupervised and reinforcement learning, and activating a subset of them to contribute to the detection process, depending on the learning conditions and application domain. The activated base classifiers will be selected to match the requirements of the environment where they will be applied.  
Conceptualization, S.I. and Z.E.M.; Software, S.I.; Data curation, S.I.; Formal Analysis, S.I. and Z.E.M.; Investigation, S.I.; Methodology, S.I. and Z.E.M.; visualization, S.I.; Writing original draft, S.I. and H.R.; review & editing, S.I. and H.R.; Project administration, S.I. and H.R.; Validation, S.I. and H.R.; Supervision, H.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Dataset is available on GitHub and other websites.  
The authors acknowledge the support of the University of North Dakota College of Engineering & Mines.  
The authors declare no conflict of interest.
# 3D Void Handling Geographic P2P-RPL for Indoor Multi-Hop IR-UWB Networks
## 2 Related Works
Additionally, the highest transmission priority is given to one-hop neighbors closest to the destination. Location information of network nodes is acquired from the proposed IR-UWB-based 3D multi-hop self-localization algorithm to enable the geographic routing methodology. Table 1 compares the RPL-based P2P routing protocols addressed in this section. GVA-P2P-RPL breaks from the convention of the RPL-based protocols' flooding strategy and exploits the greedy forwarding strategy, further reducing a large portion of routing overheads. Greedy Forwarding and Void Handling P2P-RPL with Adaptive Trickle Timer for Indoor 3D IR-UWB Networks  
In this section, we introduce the GVA-P2P-RPL, a reactive 3D geographic routing protocol, which energy efficiently, reliably and quickly discovers P2P routes for indoor 3D \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Protocol Name** & **Type** & **Routing Strategy** & **Overhead** \\ \hline P2P-RPL & Reactive & flooding whole network & High \\ LA-P2P-RPL & Reactive & flooding restricted zone & Medium \\ ER-RPL & Proactive/Reactive & flooding selected regions & Medium \\ GVA-P2P-RPL & Geographical & greedy forwarding & Low \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of RPL-based P2P routing protocols for LLNs. IR-UWB networks. To obtain location information of nodes, IR-UWB ranging-based 3D bounding-box algorithm and mobile node tracking scheme are used to estimate node positions. Using the estimated location information, GVA-P2P-RPL reduces routing overheads by greedily forwarding (unicasting) P2P-DIOs as in Figure 1c instead of flooding (broadcasting) them as shown in Figure 1a,b. In 3D environments, the local minimum problem occurs more often than in 2D [7]. In the void handling procedure, P2P-DIO messages are temporarily broadcast until the local minimum problem is overcome. After that, the greedy forwarding strategy is used again. This switch between unicasting and broadcasting is repeated until the destination is reached to cope with the local minima and ensure route discovery success. Adaptive trickle timer (ATT) in 3D GVA-P2P-RPL is also proposed, which uses a shorter minimum trickle timer interval \(l_{min}\) parameter value. The ATT algorithm adaptively shortens the listen-only periods by giving higher transmission priority to nodes closer to the destination. As a result, the route discovery time is shortened, and collisions among P2P-DIO message transmissions are better avoided.
## 3 Results
### Results and Analysis
#### 3.3.5 Ablation Experiments  
In Section 3.3.3, the target task networks were trained and tested using a single feature. In Section 3.3.4, the three-feature fusion network based on MFsNetV1 was trained and tested. In this subsection, the network structure in Figure 12 was utilized for training and testing the fusion of two features, following the same procedure as in Section 3.3.4. All results are summarized in Table 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Data Type**} & \multicolumn{2}{c}{**Classification Results**} & \multirow{2}{*}{**Recall Rate**} \\ \cline{2-3} \cline{5-5}  & **Interference** & & **Underwater Vehicle** \\ \hline Type I interference A & 32 & 0 & 100 \% \\ Type I interference B & 31 & 1 & 96.9\% \\ Type II interference & 25 & 0 & 100\% \\ Type III interference & 24 & 1 & 96.0\% \\ Type IV interference & 24 & 1 & 96.0 \% \\ Underwater vehicle & 3 & 63 & 95.4\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Test results of the MFsNetV1.  
Figure 18: Validation curves for the feature fusion classifier.  
Specifically, the test accuracy improves from 93.6% to 96.6% when fusing the spatial feature P3 SF with the time-frequency feature ETFF, from 93.6% to 95.6% when combining P3 SF with the Doppler feature DFSDF, and from 90.2% to 96.1% when fusing ETFF with DFSDF. The accuracy increases to 97.1% when the three features were fused.
# Algorithm for Generating S-Boxes with Prescribed Differential Properties
## 3 New Algorithm for Generating S-Boxes with Prescribed Differential Properties
### Partial DDT  
Let \(S:\mathbb{Z}_{2}^{n}\rightarrow\mathbb{Z}_{2}^{m}\) be a vectorial Boolean function. We say that \(S\) is partially determined on \(P\) if we have a set of points \(P=\{(x,y)\in X\times Y\}\) with \(X\subset\mathbb{Z}_{2}^{n}\), \(Y\subset\mathbb{Z}_{2}^{m}\), and \(y=S(x)\) for each \(x\in X\). The Partial Difference Distribution Table of partially determined \(S\) is a \(2^{n}\times 2^{m}\) matrix with elements  
\[PDDT_{a,b}=|\{x\in X;\quad S(x\oplus a)\oplus S(x)=b\}|.\]  
We can compute the partial DDT of an S-box given lists \(X,Y\) with Algorithm 1. We suppose that lists \(X,Y\) encode pairs \((x,y=S(x))\) in a given order of elements. Note that if lists \(X,Y\) are extended by one element, the next PDDT can be computed efficiently by simply adding extra values to the previous PDDT by computing differences of the last added pair \((x,y)\) with previous pairs in the partial S-box.  
```
0:\(X,Y\) [Lists defining a partial S-box of the same length \(l\).]
0:\(n,m\) {S-box dimensions.} \(PDDT\gets zero\_matrix(2^{n},2^{m})\) for all\(i\in\{0,1,\ldots,l-1\}\)do for all\(j\in\{0,1,\ldots,l-1\}\)do \(xor\_x\gets X[i]\oplus X[j]\) \(xor\_y\gets Y[i]\oplus Y[j]\) \(P[xor\_x][xor\_y]\gets P[xor\_x][xor\_y]+1\) endfor endfor return\(PDDT\)
```  
**Algorithm 1** Partial DDT construction algorithm  
Because \(X\subset\mathbb{Z}_{2}^{n}\), each \(PDDT_{a,b}\leq DDT_{a,b}\). Thus, given a partially evaluated \(S\), we can check whether \(PDDT\) satisfies our pre-selected threshold \(\delta\). If for some \(a,b\), \(PDDT_{a,b}>\delta\), then \(DDT_{a,b}>\delta\) as well. Thus, we can quickly reject partial \(S\) without evaluating other points from \(\mathbb{Z}_{2}^{n}\setminus X\) and use backtracking to check for other more suitable branches of the search tree.  
Note that we can place other stricter criteria on DDT. However, we require that these criteria are preserved when using partial DDT, e.g., we can have more strict restrictions on specific rows or columns of the DDT. This can be useful for cipher designs based on simple substitution permutation networks, e.g., a present-like cipher [47] with a custom S-box, which requires stronger criteria on rows and columns of the DDT indexed by indices with low Hamming weight. In general, we can define a function \(satisfies\_conditions()\) with the input being a partial DDT, which returns **true** if the partial DDT satisfies S-box criteria and **false** otherwise. In our analyses, we use a simple function that only checks whether \(PDDT_{a,b}\leq\delta\) for each \(a,b\).