esa-sceva/satcom-chunk-collection huggingface dataset:
# On Modeling Eavesdropping Attacks in Underwater Acoustic Sensor Networks +
## 5 Empirical Results  
In this section, we conduct extensive simulations to evaluate the accuracy and the effectiveness of our proposed analytical model. As indicated in the previous work [40], the border effect often affects the accuracy of the simulation results. In this paper, we consider a simplified approach to eliminate the border effect. Specifically, we place the eavesdropper at the center of simulation area, which is large enough so that the border effect can be ignored.  
The statistical eavesdropping probability denoted by \(P_{s}(E)\) is calculate by the following equation  
\[P_{s}(E)=\frac{\text{No. of topologies that have been eavesdropped}}{\text{No. of topologies}} \tag{20}\]  
The physical meaning of Equation (20) is the percentage of the number of topologies being eavesdropped to the total number of topologies. As shown in the previous work [40], when the total number of topologies \(\rightarrow\infty\), we can accurately approximate the theoretical result. In practice, we generate \(10,000\) random topologies for each run of simulations in this paper (to balance the accuracy against the computational complexity).  
Then, we conduct extensive simulations by dividing them into two groups according to impacts of different eavesdropper: isotropic eavesdropper and array eavesdropper (\(M=10\)). In each group, we investigate the eavesdropping probability in IUSNs and AUSNs, respectively. Note that we assume both IUSNs and AUSNs have the same network topologies and environment conditions with a fixed threshold of signal-to-noise threshold \(\Delta_{0}=20\,\mathrm{dB}\).
# Plum Tree Algorithm and Weighted Aggregated Ensembles for Energy Efficiency Estimation
## 5 PTA and Machine Learning Ensembles for Energy Efficiency Estimation
### PTA for Ensemble Weight Optimization for Energy Efficiency Estimation
Testing Data, and the number of samples \(NS\) of the Standardized Testing Data, the objective function \(OF\) has the following formula:  
\[OF=\frac{1}{2\times NS}\sum_{j=1}^{NS}\left(z_{j}^{\prime}-z_{j}\right)^{2}, \tag{10}\]  
such that:  
\[z_{j}^{\prime}=w_{RR}^{\prime}\times y_{RFR_{j}}+w_{CRR}^{\prime}\times y_{GRR_{ j}}+w_{Ash\text{Boust}}^{\prime}\times y_{Ash\text{Boust},j}+w_{ETR}^{ \prime}\times y_{ETR,j},\text{ where }j=\overline{1,NS}. \tag{11}\]  
**Step 6. **: The optimized ensemble weights are the ones which are computed from the \(p_{gbest}\) value. **Step 7. **: The ensemble is evaluated using the Root-Mean-Square Error (RMSE), R-squared (\(R^{2}\)), the Mean Absolute Error (MAE), and the Mean Absolute Percentage Error (MAPE), represented by Formulas (12)-(15), respectively:  
\[RMSE=\sqrt{\frac{\sum_{j=1}^{NS}\left(z_{j}-y_{j}\right)^{2}}{NS}}, \tag{12}\]  
\[R^{2}=1-\frac{\sum_{j=1}^{NS}\left(z_{j}-y_{j}\right)^{2}}{\sum_{j=1}^{NS} \left(z_{j}-\overline{y}\right)^{2}}, \tag{13}\]  
\[MAE=\frac{\sum_{j=1}^{NS}\left|z_{j}-y_{j}\right|}{NS}, \tag{14}\]  
\[MAPE=\frac{1}{NS}\times\sum_{j=1}^{NS}\frac{\left|z_{j}-y_{j}\right|}{z_{j}}, \tag{15}\]  
such that \(y\) represents the values predicted by the ensemble, and \(\overline{y}\) is defined as:  
\[\overline{y}=\frac{\sum_{j=1}^{NS}y_{j}}{NS}. \tag{16}\]
## 2 Constraint Analysis at the Velocity Level
Note that the extended joint velocity, \(\dot{\mathbf{q}}_{e}=[\dot{\mathbf{q}}^{T};\mathbf{0}^{T}]^{T}\) is composed of usual joint velocity \(\dot{\mathbf{q}}\) and zero for constraints \(\mathbf{J}_{c}\). Generally, the inverse rate kinematics of Equation (8) can be obtained by inverting \(\mathbf{J}_{e}\) in Equation (8):  
\[\dot{\mathbf{q}}_{e}=\mathbf{J}_{e}^{-1}\dot{\mathbf{x}}=\mathbf{G}_{e}^{T}\dot{\mathbf{x}}= \begin{bmatrix}\mathbf{G}_{j}^{T}\\ \mathbf{G}_{c}^{T}\end{bmatrix} \dot{\mathbf{x}}, \tag{10}\]  
where \(\mathbf{G}_{e}^{T}\) is the inverse of \(\mathbf{J}_{e}\), and \(\mathbf{J}_{e}\) is a \(6\times 6\) extended Jacobian comprising the two complementary orthogonal sub-spaces called IMS and IRS given as follows;  
\[\mathbf{J}_{e}= \begin{bmatrix}\mathbf{J}_{j}&\mathbf{I}_{c}\end{bmatrix} , \tag{11}\]  
\[\mathbf{G}_{e}= \begin{bmatrix}\mathbf{G}_{j}&\mathbf{G}_{c}\end{bmatrix} . \tag{12}\]  
Since our objective is to utilize the constraint relation, we will be restricted to focus on obtaining \(\mathbf{J}_{c}\) and \(\mathbf{G}_{c}\).
# Resilience of LTE-A/5G-NR Links Against Transient Electromagnetic Interference
## 3 Materials and Methods
### Types of Unintentional Interferences  
Unintentional interference such as transient interference can come from different sources. In the railway environment, unintentional transient interference is produced by high-power generators and the arcing effect of pantograph-catenary contact. Unintentional interference can arise from numerous sources, such as switching of power supplies or operation of high-power motors. A notable example is the EMI produced when pantographs on trains come into contact with catenary wires, as shown in Figure 1.  
Currently, innovations in power supply systems for road transport have aimed to reduce emissions, but they also introduce potential sources of transient EMI. As shown in Figure 2 (generated with the assistance of GenAI ChatGPT Turbo version 4.1), the movement of vehicles on highways equipped with these systems can inadvertently interfere with communication links. The effect can be severe if tram overhead lines pass through the city centre or critical infrastructure, which can disrupt communication links such as Long Range (LoRA), Wi-Fi, and LTE-A [5]. Another source of unintentional interference is the SPN-43 radar, which operates in the S-band for the location of military targets, which can disrupt nearby communication systems. Furthermore, dynamic spectrum sharing (DSS)  
Figure 1: Catenary– pantograph contact.  
in the citizens broadband radio service (CBRS) spectrum improves spectrum efficiency, but it can also introduce interference between shared bands, challenging communication reliability [3].
# Daily Flood Monitoring Based on Spaceborne GNSS-R Data: A Case Study on Henan, China
## 4 Discussion  
We applied quality control on the CYGNSS data to remove outliers. Then the CYGNSS measurements with a period of one day were gridded. Afterwards, we obtained the occurrence and the duration of the flood based on the CYGNSS observations. In the next step, a simple threshold method was used to monitor the inundated areas as well as the shift and distribution of the areas severely affected by the flood. For inundated areas, using different thresholds will lead to different results. In general, the threshold is related to the terrain and vegetation coverage of the study area. More studies are needed to test the influence of vegetation and roughness on the threshold.  
Similarly, based on the brightness temperature and soil moisture changes from SMAP, we generated a temporal map to show the evolution of the flood. However, the monitoring results are limited by the temporal resolution of SMAP. There may also be differences with CYGNSS in terms of the occurrence and duration of floods.  
When the flood occurred, the brightness temperature, soil moisture from SMAP and CYGNSS reflectivity changed obviously in Zhengzhou and Xinxiang. This shows that their monitoring results are in good agreement. The daily changes are not captured by SMAP; in contrast, CYGNSS can provide daily monitoring results, and the spatial resolution is also better than SMAP. It is, therefore, obvious that CYGNSS high temporal and spatial resolution data can monitor floods in more detail and faster. Comparing the change of CYGNSS reflectivity with the optical image, it is also found that they are in good agreement. It further proves that the CYGNSS results are relatively accurate and can be applied to flood monitoring.
## 4 Analysis and Results
### Dynamic Analysis
#### 4.2.2 Case II--Flexion  
A summary of results is presented in Figure 10 for the set of experiments where the cylindrical end effector strikes the index finger from the top, coming vertically downward. The velocity of the end effector was varied from 1.0 m/s to 2.2 m/s, and experiments were repeated for effective mass values corresponding to small-, medium-, and heavy-payload-capacity robots (UR3, UR5, and UR10). Figure 10c shows the contact force at the point of contact of the end-effector with the index finger. Technical Specification ISO/TS 15066 specifies a maximum permissible force of 140 N for quasi-static contact, with a permissible force/pressure multiplier of 2 for transient contact. Our analysis suggests a strong correlation between contact forces and principal stresses. The results suggest that allowing a force multiplier of 2 resulted in exceedingly high principal stresses in the bones.
# Compaction Characteristics and Mechanical Response of Gravel-Glass-Rubber Mixtures +
## 5 Potential Use of GGRM as Fill Materials in Geotechnical Applications
Specifically, based on the mechanical responses observed in direct shear and one-dimensional compression tests and considering the behavioral zones described earlier in Table 4 and reported in Figure 17, the following recommendations can be made for GGRM (\(R_{\text{B}}\) = 0.4 and 0.55):  
* _GS mixtures_ (0.3 \(\leq R_{\text{B}}\) < 0.6 and \(G_{\text{R}}\) > 0.4)--due to their high strength and limited compressibility, they can be used as structural fills. Yet, in situations where high vertical loads are applied, improvement by preloading or other ground improvement techniques should be considered to enhance their compressibility. * _GSC mixtures_ (0.3 \(\leq R_{\text{B}}\) < 0.6, \(G_{\text{L}}\) \(\leq\) 0.4, and \(G_{\text{R}}\) \(\leq\) 0.4)--due to their compressibility and potential high crushability, they should only be employed as structural fills if improved, for instance, by preloading; alternatively, they can be safely used as general fills. * _SC mixtures_ (0.3 \(\leq R_{\text{B}}\) < 0.6 and \(G_{\text{L}}\) > 0.4)--due to their higher compressibility and high crushability, their use as structural fills is discouraged unless improved. But they are acceptable as general fills. For other GGRM materials not directly investigated in this study but of practical interest, the following recommendations can be made:
* _G mixtures_ (\(R_{\text{B}}\) < 0.3 and \(G_{\text{L}}\) < 0.3)--they can be safely used as structural fills due to their high frictional resistance and minimal compressibility [38]. * _GC mixtures_ (\(R_{\text{B}}\) < 0.3, \(G_{\text{L}}\) \(\geq\) 0.3, and \(G_{\text{R}}\) \(\geq\) 0.3)--they should be employed as structural fills only if improved, for example, by preloading, because of their tendancy to exhibit high crushability. Otherwise, they can be safely used as general fills. * _C mixtures_ (\(R_{\text{B}}\) < 0.3 and \(G_{\text{R}}\) < 0.3)--likewise, _GC mixtures_ should be employed as structural fills only if improved due to the high crushability of the glass. Otherwise, they can be safely used as general fills. * _S mixtures_ (\(R_{\text{B}}\) \(\geq\) 0.6)--not only are they highly compressible, but from an environmental viewpoint are of concern due to the high concentration of metal leachate from the rubber inclusions and the associated risk of contamination to groundwater and soil [40,41]. While they could be potentially suitable as general fills (from a geotechnical viewpoint), they must be pretreated to reduce their environmental impact before being used as filling materials. Alternatively, they must be enclosed by  
Figure 18: Potential use of compacted GGRM as fill materials in geotechnical applications (data points from: [PERSON] et al. [38], [PERSON] et al. [54] and this study). impermeable geosynthetic membranes to prevent contamination of the surrounding environment [40; 41].
## 4 Discussion and Future Directions  
Reframing LRM limitations as manifestations of bounded rationality fundamentally changes how we evaluate these systems. Rather than viewing performance collapse and effort reduction as failures, we can understand them as evidence of sophisticated resource management strategies that emerge naturally from systems operating under computational constraints.  
This perspective suggests that current LRMs may be more cognitively sophisticated than previously recognized. The ability to adaptively allocate computational resources based on implicit assessments of task tractability represents a form of metacognitive awareness that parallels human cognitive monitoring systems. In this context,'metacognitive' refers to system-level adjustments based on internal signals, not conscious reflection. The similarity to human processes is functional, not literal.  
A recent methodological analysis by [PERSON] reveals that many \"reasoning failures\" documented in the Apple study stem from experimental design issues rather than cognitive limitations [9]. This analysis demonstrates that models explicitly recognize output constraints (\"The pattern continues, but to avoid making this too long, I'll stop here\"), that River Crossing puzzles with N \(\geq\) 6 are mathematically impossible with the given boat capacity, and that alternative representations (requesting generating functions instead of exhaustive move lists) restore high performance on previously \"failed\" problems.  
This methodological critique aligns powerfully with my dual-process reinterpretation. If the \"illusion of thinking\" is itself illusory, arising from evaluation artifacts rather than genuine reasoning deficits, then the patterns I identify as manifestations of bounded rationality become even more compelling. The reduction in reasoning tokens at high complexity may indeed reflect sophisticated resource management: models recognize when exhaustive enumeration becomes impractical and adaptively shift to more efficient representations or strategic truncation.  
This convergence of methodological critique and theoretical reframing suggests that LRM behavior reflects neither illusion nor failure, but rather adaptive computational strategies that parallel human cognitive resource allocation. The apparent \"collapse\" may represent rational disengagement from tasks that exceed practical constraints rather than fundamental reasoning limitations.
## 1 Introduction
### LoRaWAN  
Most connected devices use LPWAN networks, such as LoRaWAN, SigFox, and NB-IoT (more details about technical differences between these three technologies are available in [3]). With LoRaWAN small amounts of data can be transmitted through long distances, with low energy consumption. Three frequency bands are used: 868 MHz in Europe,915 MHz in America, and 433 MHz in Asia with bandwidths 125,250 and 500 kHz. It uses two modulation types: frequency shift keying (FSK) and chirp spread spectrum (CCS); it offers data rates from 250 to 5.5 kb/s with CCS and up to 50 kb/s with FSK, and allows a payload of 243 bytes. This technology allows up to 10 Km as a communication range in urban zones and 20 km in rural zones, and can save battery life up to 5 years. LoRaWAN is a low cost solution, costs less than USD 5.00 of the device cost, and less than USD 1.00 per device per year of the operator subscription [3]. All of these features combined make LoRaWAN a powerful technology for IoT [4]. However, like any IoT terrestrial networks, this technology does not offer global coverage.
# Insights from Learning Analytics for Hands-On Cloud Computing Labs in AWS
## 3 Educational Data Analytics
### Statistical Variables Defined  
The following variables are defined:  
* _CTOCA_, which represents the mark obtained in the final timed exam that students undertake. They need to achieve a mark greater than or equal to 5 in order to obtain the certificate of achievement. These data are collected from the LMS.
* _CTAMI_ through _CTAM7_, which indicate the results of the optional self-assessment test carried out by the student after each one of the seven modules, ranging from 0 to 10. This value may be absent for students that did not perform the test. These data are collected from the LMS. The variable _CTAMT_ is the average of the seven aforementioned variables, where 0 is assigned for a non-taken self-assessment test for the corresponding module.
* _PL_X_ where X = EC2, EC2_S3, RDS, APP, CF, VPC, and LAMBDA_SQS, which indicate the percentage of progress of a student in each lab activity for each student (in order of appearance during the course). These data are collected by CloudTrail-Tracker. The variable _PL_TOTAL_ is the average of the seven aforementioned variables, where 0 is assigned for a lab activity not carried out by the student. For the sake of completeness, a brief summary of the lab activities is included:
* PL_EC2: Students deploy and configure virtual machines (VMs) with different operating systems using Amazon EC2.
* PL_EC2_S3: Students create fleets of VMs that can grow and shrink depending on the workload being processed. They also interact with Amazon S3 to perform object storage for the management of files.
* PL_RDS: Students use Amazon RDS to manage the lifecycle of relational database management systems (RDBMS), such as MySQL, using fault-tolerant deployment of databases.
* PL_APP: Students deploy a highly available architecture to support the Wordpress blogging platform by replicating and distributing the internal components required.
* PL_CF: Students use Amazon CloudFormation to deploy complete application architectures described in template files using an Infrastructure as Code (IaC) approach.
* PL_VPC: Students create isolated networking sections in AWS to better increase the security of the deployed applications by using Amazon VPC (Virtual Private Cloud).
* PL_LAMBDA_SQS: Students create serverless event-driven applications to process files using queue services such as Amazon SQS and Functions as Service (FaaS) approaches via AWS Lambda.  
It is important to point out that these are not the only lab activities carried out throughout the course, but those from which automated metrics can be obtained via CloudTrail-Tracker. However, these lab activities are spread across the course and, therefore, represent a good proxy for the amount of work carried out by the students when performing their practical educational activities in AWS.
## 5 Experimental Results and Analysis
### Evaluation Metrics  
The indicators used to evaluate the prediction model in this article are accuracy, precision, recall, specificity, \(F1\) score, and AUC value.  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}\times 100\% \tag{2}\]  
\[Precision=\frac{TP}{TP+FP}\times 100\% \tag{3}\]  
\[Recall=\frac{TP}{TP+FN}\times 100\% \tag{4}\]  
\[Specificity=\frac{TN}{TP+FN}\times 100\% \tag{5}\]  
\[F1=\frac{2 TP}{2 TP+FP+FN}\times 100\% \tag{6}\]  
In Formulas (2)-(6), \(TP\) means that the correct prediction is a positive example; that is, the prediction is a positive example and the prediction result is correct [1]. In Formulas (2)-(6), \(TP\) means that the correct prediction is a positive example, and \(TN\) means that the correct prediction is a counterexample. \(FP\) means that the wrong prediction is a positive example. For example, \(FN\) indicates that the wrong prediction is a counterexample [2;3].
# Behavior-Based Control Architecture for Legged-and-Climber Robots
## 1 Introduction
Some important definitions should be declared in a control architecture: (a) an agent is a computer system that is capable of autonomous action in its environment to achieve its delegated objectives [7,8], (b) a level is composed of an agent or a group of agents that have the same importance from a determined point of view, (c) hierarchy is how agents of higher levels have a kind of control over the agents of lower levels, (d) a centralized control is characterized by coordinated conduct of the agents, whose decisions depend directly on the state of the other agents, (e) a decentralized control is characterized by freedom in the agent decisions, without directly considering the state of the rest of the agents, and (f) a behavior-based control is a way in which agents are delegated with the main task to achieve a goal through some instructions. Then, it is possible to conclude that a control architecture may be understood as a multi-agent system where the communication rules and protocols are well-defined. This paper's objective is to design an organic and hierarchical control that allows the safe movement of the legs. Moreover, the control must be generic for a legged robot with any number of legs. In this paper, we test the performance of the control architecture over the legged-and-climber robot ROMHEX. This robot has ben modified; the most remarkable is the change of the initial position of each leg that the robot should adopt to maximize the efficiency of the walking pattern. This maximization has been obtained generically, as explained in Section 3.1. Thus, the initial position directly influences the beginning of the gait, and it is also used as the default position when the robot needs to reconfigure while walking. We propose a control architecture that fulfills the following requirements: (a) generic for all legged robots, independent of the number of legs, distribution of legs in the body, or the number of joints per leg, (b) agent-based, (c) hierarchical (that is, agents of higher levels should have the control of lower-level agents, and in this way, first agents may disable second agents), and (d) agents of the same level must synchronize their behaviors using synchronization mechanisms. This paper is organized as follows: In Section 2, we present an overview of the state of the art of control architectures. We describe how different architectures are organized and the levels they use. In Section 3, we describe the hexapod robot used in the tests. Furthermore, we include the developed optimization of the legs' position in the body. In Section 4, we describe the developed control architecture, and we explain its levels, hierarchy and agents. In Section 5, we discuss the results obtained during the tests. Lastly, in Section 6, we detail the obtained conclusions from the results.