esa-sceva/satcom-chunk-collection huggingface dataset:
# RunsGuard Framework: Context Aware Cricket Game Strategy for Field Placement and Score Containment
###### Abstract  
Sports analytics utilizes data analysis techniques and computational methods to gain insights, make informed decisions, and facilitate improvements in the performance of individuals and teams. Cricket is one of the most popular games and continues to evolve worldwide. The availability of ball-by-ball data demands in-depth investigation of player strategies, team dynamics, and the impact of contextual variables. Existing studies explored various aspects of cricket analytics, including detecting key events, predicting outcomes, and ranking teams. However, the literature lacks a comprehensive integrated framework that processes unstructured sports commentary, extracts actionable insights, conducts a thorough player analysis, and develops strategic plans while considering contextual factors. This work aims to propose a bowling and fielding strategy to contain a batsman. For this purpose, we developed a comprehensive context-aware framework that collects data, extracts insights from commentary, identifies player strengths and weaknesses, and proposes cricket bowling and fielding strategies according to the given context. To evaluate this work, we implemented a case study that simulated different scenarios, and our framework suggested bowling and fielding strategies. In these simulations, the proposed strategies consistently demonstrated a substantial reduction in the number of runs that were scored. On average, the framework reduces the batsman's score rate by 33%. These findings underscore the practical effectiveness of research in optimizing field placement and effectively reducing scoring opportunities. Finally, by bridging the gap between data analytics and cricket game strategy, this methodology provides a competitive advantage to coaches, captains, and players. In the future, we aim to involve temporal patterns to understand the evolving behavior of players.  
performance analysis; field placement strategy in cricket; data-driven planning; score containment in cricket; ball-by-ball cricket data features; batsman behavior modeling; strengths and weaknesses analysis; cricket commentary analysis Article
# Input-to-State Stability of Variable Impedance Control for Robotic Manipulator
## 3 ISS of Variable Impedance Model
### ISS Proof
If the Equation (22) is satisfied, Equation (21) can be expressed as following simplified form:  
\[\dot{V}\leq-\gamma_{1}(\left\|x\right\|)+\gamma_{2}(\left\|d\right\|), \tag{23}\]  
where \(\gamma_{1}\) belongs to a class \(K_{\infty}\) and \(\gamma_{2}\) a class \(K\) function. Therefore, we can conclude that the VIM (closed-loop control system) of Equation (10) is ISS under two conditions of Equations (12) and (13). In the above Theorem 2, the sufficient conditions for the variable impedance control to stabilize the closed-loop control system have been suggested as Equations (12) and (13). In the Equation (12), the upper bound of variation of stiffness matrix was proposed, in particular, it should be smaller than the scalar multiple of the current stiffness. Thus we can say that the variation of stiffness matrix has its upper bound by the function of the current stiffness matrix. Also, Equation (13) shows that the matrix lower bound of a sum of stiffness and damping matrices should be larger than the variation of the inertia matrix. Here, \(\dot{H}(q)\) has the following property,  
\[\dot{H}(q)=C^{T}(q,\dot{q})+C(q,\dot{q}) \tag{24}\]where \(C(q,\dot{q})\) is a Coriolis and centrifugal matrix, and refer [12] for more information. Skew-symmetric property of \(\dot{H}(q)-2C(q,\dot{q})\) is well-known in the robotics and plays a core role in proving the stability. Above Equation (24) can be easily derived like this:  
\[\dot{H}(q)-2C(q,\dot{q}) =-(\dot{H}(q)-2C(q,\dot{q}))^{T} \tag{25}\] \[=-\dot{H}(q)+2C(q,\dot{q})^{T}.\]  
Since the Coriolis and centrifugal matrix is bounded by the joint velocity of a robotic manipulator with some scalar, i.e., \(\|(C(q,\dot{q}))\|\leq c_{0}\,\|\dot{q}\|\), we can conclude that the sum of stiffness and damping matrices should be larger than some scalar multiplication of joint velocity. For practical use, the above conclusion can be interpreted that as the joint velocity becomes fast, so the stiffness matrix should be larger and _vice versa_.
# Detection of Induced GNSS Spoofing Using S-Curve-Bias
## 4 Experiments
### Introduction of TEXBAT Data Sets  
The radionavigation laboratory in the University of Texas at Austin produced the first public database (ds1-ds6) of signals affected by several types of spoofing attacks concerning GPS satellites in 2012. Two additional data sets, ds7 and ds8 were added to TEXBAT in August 2015. Therefore, there were eight different GPS L1 C/A spoofing data sets in TEXBAT [39; 42], namely ds1-ds8, which represent different spoofing attack scenarios. They were based on two \"clean data sets\" replayed through the vector signal generator, and 25 Msps sampling rate data grabber attached in one case to a static antenna building on the campus of the University of Texas and in the other case to an antenna mounted on a vehicle, which travelled across the city [39].  
In this paper, ds7 was chosen to evaluate the performance of the proposed method. In this scenario, it represented the so-called seamless takeover attack. There were no offsets in samples and time, and spoofing was already perfectly aligned since it was digitally added to the clean data sets. No obvious disruption can be observed in the tracking loop. The ds8 represents a zero-delay security code estimation and replay (SCER) attack, which was identical to the ds7, except for the spoofer guesses, and generated the navigation data bits in real time [42]. Since the proposed method did not employ security codes to detect spoofing attacks, its anti-spoofing performance in ds7 and ds8 will be the same. Therefore, ds8 was not be considered in the paper.  
For ds7, spoofing is free from 0 to 110 s, data is identical to \"clean static date sets\" during this time. The spoofing signals are injected for each GPS L1 C/A signals at the 110-th second and the amplitude of spoofing varied nonlinearly. After that, the code phases of the spoofing relative to the counterpart authentic ones increased at a rate of 1.2 m per second, that is, \(409.2\times 10^{-5}\) Hz. Finally, a 1.27 us clock offset is induced in the victim receiver which is described in the reference [42].
# Development of Path Planning Tool for Unmanned System Considering Energy Consumption
## 2 Mission Hierarchy  
Figure 1 depicts the overall mission hierarchy to achieve the most energy-efficient path and corresponding estimated SOC value.  
Figure 1: Mission hierarchy.  
First, using a Google Earth satellite photograph, and the takeoff and landing locations, the most energy-efficient path is determined by the path-planning part. Here, the path is saved in both the Cartesian system unit, \(Trajectory_{xyz}\), and the geographic coordinate system unit, \(Trajectory_{GPS}\).  
Second, the achieved path is utilized for the indoor flight simulation and outdoor flight experiment to calculate the cell open-circuit voltage (OCV), \(v_{ocr}\), and load current, \(i_{o}\), data.  
Third, the achieved cell voltage and current data are utilized for the battery state estimation to calculate the SOC. Both SOC\({}_{Sim}\) and SOC\({}_{Exp}\) are calculated to compare results.  
With the achieved SOC value, one can finally determine whether the EEPP algorithm results in an energy-efficient path.
## 3 Dihydroxybenzene-Based Building Blocks (Catechol)
### Alkylcatechols  
Alkylcatechols, such as urushiol and its analogs, are found in lacquer trees. These are amphiphilic molecules that consist of two parts: a relatively hydrophilic head (i.e., catechol) and a hydrophobic tail (i.e., alkyl chain) [60], which can be assembled into polyphenic materials. Assembled polyphenols have been adopted as protective coating materials for furniture and industrial equipment owing to their strong shear strength, as well as their chemical resistance to heat and moisture [61]. The advantage of alkylcatechols is that they can be prepared as an emulsion due to their amphiphilicity, which allows the lacquer to be extremely concentrated in low amounts of water. Concentrated catecholic extracts allow direct surface coating with simple painting because of their sticky nature. Subsequently, oxidative polymerization between urushiol and its analogs continues, resulting in a stable polyphenic layer similar to that produced through the self-assembly mechanisms of the previously described catecholic building blocks. An important aspect to note is that an oxygen source that involves oxidative polymerization is mainly provided from the ambientair in alkylcatechol-based extraction rather than the solution. Moreover, the quinone formation that promotes polyphenol development can be accelerated by a naturally occurring oxidase (e.g., lacase) that exists in the lacquer tree [62]. Catechol-metal complexation or additional crosslinking reactions at high temperatures could also promote and stabilize the self-assembly of urushiol [63]. Moreover, the oxidative crosslinking of urushiol can also be activated through UV-induced photocuring, similar to the mechanism underlying catechin assembly [64].
# Digital Twin-Based Automated Guided Vehicle Scheduling: A Solution for Its Charging Problems
## 4 Case Study
### Results and Discussion
The makespan using the DTDAS proposed in this paper is 1472 s, reduced by 10.7% compared with traditional dynamic scheduling. Additionally, the total energy consumption is reduced by 1.32%. In addition, as shown in Figure 15b, the charging process of AGV 3 is inserted before the transportation task 2-3. Compared with the initial scheduling plan, the charging task triggered by the DTDAS just used the idle period of AGV 3, and the charging time is relatively short. The AGV does not have to wait until the battery is fully charged, and the power of this charging process will be determined according to the follow-up task arrangement of this AGV. It can be seen from Figure 16 that the amount of charge can just meet the needs of completing the transportation task of this batch of orders. In this way, not only can the task completion time be shortened, but it also has the effect of effectively using power resources. The scheduling plan including the charging schedule has almost no impact on the distribution of transportation tasks. The time to complete the task is almost the same as the initial scheduling. Compared with the traditional dynamic scheduling method, the DTDAS proposed in this paper can not only shorten the makespan and reduce energy consumption, but also save a lot of rescheduling computing resources.
# Robust Clustering Routing Method for Wireless Sensor Networks Considering the Locust Search Scheme
###### Abstract  
Multiple applications of sensor devices in the form of a Wireless Sensor Network (WSN), such as those represented by the Internet of Things and monitoring dangerous geographical spaces, have attracted the attention by several scientific communities. Despite their interesting properties, sensors present an adverse characteristic: they manage very limited energy. Under such conditions, saving energy represents one of the most important concepts in designing effective protocols for WSNs. The objective of a protocol is to increase the network lifetime through the reduction of energy consumed by each sensor. In this paper, a robust clustering routing protocol for WSNs is introduced. The scheme uses the Locust Search (LS-II) method to determine the number of cluster heads and to identify the optimal cluster heads. Once the cluster heads are recognized, the other sensor elements are assigned to their nearest corresponding cluster head. Numerical simulations exhibit competitive results and demonstrate that the proposed protocol allows for the minimization of the energy consumption, extending the network lifetime in comparison with other popular clustering routing protocols.  
locust search algorithm; wireless sensor networks; swarm-based algorithms; clustering routing protocol; energy-efficiency; metaheuristic algorithms +
Footnote †: journal: Physics Letters B
## 4 Implementation
### Experimental Evaluation Index Setting  
We evaluate the model effect based on four indicators: the false negative rate (FNR), the false positive rate (FPR), the area under the curve (AUC), and the receiver operating characteristic (ROC). The definitions of the FNR and FPR are as follows:  
\[\text{FNR} =\frac{FN}{TP+FN} \tag{9}\] \[\text{FPR} =\frac{FP}{FP+TN} \tag{10}\]  
The ROC curve and AUC are important indicators for evaluating the performance of classification models. The closer the ROC curve is to the upper-left corner, the better the model performance. The AUC is the area under the ROC curve. The closer the value is to one, the stronger the model's ability to distinguish between positive and negative samples. AUC equals to 0.5 means that the model's performance is equivalent to the value of the random guessing probability. A higher AUC value means the model can achieve a higher FNR while maintaining a lower FPR, thereby effectively evaluating model performance.  
During the cluster analysis process, the choice of the K value plays a crucial role in the accuracy and validity of the results. Usually, the choice of the K value requires the comprehensive consideration of the distribution of data and the specific goals of the task. This article uses the elbow method to find the \"elbow point\" where the curve change significantly slows down as it approaches the optimal K value by drawing the relationship between the number of clusters and the sum of squared clustering errors (SSE). In addition, determining the K value through the contour coefficient helps to classify different traffic behaviors more accurately, thereby effectively distinguishing complex behaviors such as single-node continuous attacks and multi-node burst attacks.  
In the setting of the ablation experiment of the embedding module, we focus on analyzing the impact of context information on model performance. The main function of the embedding layer is to combine the original features of the traffic behavior sequence with context information to generate high-dimensional feature representations so as to provide richer input features for the subsequent Transformer model. In the experiment, an ablation experiment is used to verify the impact of the embedding process on a malicious traffic monitoring system so as to prove the importance of context information for malicious traffic detection.
# Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series
## 3 Material and Methods
### Data Preparation  
#### 3.3.1 Temporal Resampling  
The optical SITS includes invalid pixels due to the presence of clouds and saturated pixels. Nowadays, the high temporal resolution of SITS is used to efficiently detect clouds and their shadows [60]. The produced masks are then used to gap-fill the cloud-covered and saturated pixels before applying supervised classification algorithms, without a loss of accuracy [63]. We use here a temporal linear interpolation for imputing invalid pixel values. Although not perfect given the non-linear phenomena observed through the SITS, using more complex methods have shown only little impact on classification performance [64]. Note also that the biggest temporal gaps in the dataset occur mainly during the winter where the vegetation is not evolving much.  
As most of the classification algorithms explored in the manuscript require a regular temporal sampling, we apply interpolation and resampling on a regular temporal grid defined with a time gap of two days. The starting and ending dates correspond to the first and last acquisition dates of the Formosat-2 series, respectively. This operation increases the length of the Formosat-2 time series from  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Classes** & **Pixels** & **Polygons** & **Legend** \\ \hline
**Wheat** & 194,699 & 295 & \\
**Barley** & 23,404 & 43 & \\
**Rapeseed** & 36,720 & 55 & \\
**Corn** & 62,885 & 83 & \\
**Soy** & 9481 & 24 & \\
**Sunflower** & 108,718 & 173 & \\
**Sorghum** & 17,305 & 22 & \\
**Pea** & 9151 & 15 & \\
**Grassland** & 202,718 & 328 & \\
**Deciduous** & 29,488 & 24 & \\
**Conifer** & 15,818 & 18 & \\
**Water** & 30,544 & 32 & \\
**Urban** & 292,478 & 307 & \\ \hline
**Total** & 1,033,409 & 1419 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of instances per class counted at pixel- and polygon-level.  
46 to 149. As some studied algorithms, such as RF, may be sensitive to this increase of the length, we also keep the original temporal sampling with interpolation.
# Deep-Learning-Based Approach for IoT Attack and Malware Detection
## 2 Datasets
### CIC IoT 2023 Dataset  
The CIC IoT 2023 dataset was used in this study to evaluate the effectiveness of deep-learning models in the field of IoT security [32]. The CIC IoT 2023 dataset was employed in this study to evaluate the effectiveness of deep-learning models in IoT security. Developed by the Canadian Institute for Cybersecurity (CIC), this dataset provides a comprehensive benchmark for IoT attacks, simulating real-world scenarios with a network of 105 IoT devices. It features 33 different types of attacks, categorized into seven groups: DDoS, DoS, Reconnaissance, Web-based, Brute Force, Spoofing, and Mirai, where IoT devices are used as both attackers and targets. This dataset is invaluable for developing and testing IoT security solutions by providing realistic data for security analytics in large-scale IoT environments. The CIC IoT 2023 dataset includes key features such as flow duration, protocol type, flag counts (FIN, SYN, RST, etc. ), and traffic rates, alongside protocols like HTTP, TCP, UDP, and ICMP. Metrics like total sum, minimum, maximum, average,  
Figure 1: Malware families count by category. and standard deviation, along with inter-arrival times and other characteristics, allow for detailed analysis of IoT network traffic. This makes it a significant resource for researchers aiming to classify and detect malicious network activities using machine and deep-learning algorithms (see Table 1). The Internet of Things (IoT) ecosystem comprises a diverse range of components. On the network side, key devices include the Asus RT-N12 router, Cisco Catalyst 3850 24 switch, and Netgear Unmanaged Switch GS308, all monitored via the Gigamon G-TAP A-TX network tap. Controllers such as the Vera Plus, Aetoec Zigbee/Z-Wave Smart Hub, and SmartThings Hub facilitate the integration and management of smart devices. The sensor suite includes the Aetoec Water Leak Sensor, Multisensor 6, Motion Sensor, and Button, alongside security devices like the Aetoec Siren, Doorbell 6, and Door/Window Sensor 7 Pro. Cameras, including models from Arlo, Dlink, Amcrest, Google Nest, and Netatmo, provide high-resolution monitoring for both indoor and outdoor security. Smart home devices further enrich the ecosystem, with products like the Phillips Hue Bridge and Bulbs, Arlo Base Station, iRobot Roomba i3+, and Amazon Echo Studio. The Raspberry Pi 4 Model B, used to simulate potential IoT attacks, underscores the role of these devices in IoT security research. This extensive setup offers a comprehensive framework for examining the interplay of IoT components, ranging from network infrastructure and sensors to
## 2 Antenna Designing and Operation Mechanism  
The proposed antenna was fabricated on an FR-4 substrate with a thickness of 1.6 mm and has a dielectric constant \(\epsilon_{r}\) of 4.4. The design comprises a KAYI Shape geometry, comprising three antennas separated by a phase symmetry of 120\({}^{\circ}\). \(L_{u}\)- and \(U_{u}\)-shaped slits and slots were introduced in the patch element for notching characteristics at WiMax and WLAN. The length and placement of these notching elements were intelligently selected to create a third band notch at the higher frequency for the elimination of the X-band of satellite communication. A defective KITE shape was carved on the ground for better impedance matching. The optimized design was simulated on electromagnetic simulation HFSS.
# Efficient Time-Series Clustering through Sparse Gaussian Modeling
## 2 Notation and Preliminaries
### Evaluation Criteria of Sparse Time-Series Clustering
#### 2.3.4 Average and Spread Difference of Two Clustering Methods  
To compare two clustering methods for multiple instances, we need to aggregate the difference in ARI values of the two methods across all instances. To this end, we use the average difference and the second moment of the difference of the two methods' ARI values.  
The _average difference_ quantifies the average performance gain (or loss) of a clustering method against another one with respect to their ARI values across multiple instances:  
\[\mathrm{Diff}_{(1,2)}=\sum_{i=1}^{N}\frac{\mathrm{ARI}^{1}(i)-\mathrm{ARI}^{2 }(i)}{N} \tag{8}\]  
We usually refer to \(\mathrm{Diff}_{(1,2)}\) as \(\mathrm{Gain}_{(1,2)}\), if \(\mathrm{Diff}_{(1,2)}>0\), as \(\mathrm{Loss}_{(1,2)}\), if \(\mathrm{Diff}_{(1,2)}<0\).  
The _spread_ corresponds to the second moment of the difference between the ARI values of two clustering methods across multiple instances:  
\[\mathrm{Spread}=\sum_{i=1}^{N}\frac{(\mathrm{ARI}^{1}(i)-\mathrm{ARI}^{2}(i)) ^{2}}{N} \tag{9}\]  
where \(\mathrm{ARI}^{1}(i)\) (resp. \(\mathrm{ARI}^{2}(i)\)) denotes the ARI value of algorithm 1 (resp. 2) for instance \(i\) and \(N\) is the total number of instances.
## 4 Model and Notations
### Mixed-Integer Linear Programming  
In this section, the MILP formulation presented in [3] is recalled using the notation of Section 4 and afterwards extended in Section 4.1.2 by adding resource calendar constraints.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Problem Data** & \\ \hline \(i,i^{\prime}\) & Index for jobs where \(i,i^{\prime}\in\{1,\ldots,N\}\). \\ \(j\) & Index for operations. \\ \(O\) & The total number of operations. \\ \(O_{ij}\) & The \(j^{\text{th}}\) operation of job \(i\in\text{N}\). \\ \(k\) & Index for machines where \(k\in\{1,\ldots,m\}\). \\ \(M\) & Number of all material resources. \\ \(N\) & Number of jobs to be scheduled. \\ \(I_{i}\) & Set of operations of job \(i\in\text{N}\). \\ \(P_{i}\) & Processing time job \(i\in\text{N}\). \\ \(d_{i}\) & Due date of job \(i\in\text{N}\). \\ \(m_{j}\)\(\subset\)\(M\) & Set of material resources that can perform the operation \(j\in ji\). \\ \(St_{iji^{\prime}k}\) & Setup time to pass from the execution of an operation \(O_{j}\) to \\ \(BigM\) & operation \(O_{j}\) on machine k. \\ \(BigM\) & A very large number. \\ \(m_{ij}\cap m_{ij^{\prime}}\) & Set of machines on which operations \(j\) of job \(i\) and \(j^{\prime}\) of job \(i^{\prime}\) can be processed. \\ \(I_{k}\) & The number of unavailability periods on machine \(k\in m\). \\ \(v_{k}^{l}\) & The starting time of the \(lth\) unavailability period of material resource \(k\in m\). \\ \(v_{k}^{l}\) & The ending time of the \(lth\) unavailability period of material resource \(k\in m\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Notation used for the problem data.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Decision Variables** & \\ \hline \(X_{ijk}\) = & 1 if the operation \(\text{O}_{ij}\) is assigned to the material resource \(k\). \\ \(Y_{iji^{\prime}jk}\) = & 0 otherwise. \\ \(S_{ijk}\) = & 0 otherwise. \\ \(C_{ijk}\) = & Starting time of the operation \(\text{O}_{ij}\) on machine \(k\). \\ \(C_{i}\) = & Completion time of job \(i\). \\ \hline \hline \end{tabular}
\end{table}
Table 3: The notation used for the decision variables.