esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Specific Diversity and Relative Abundance of the Sampled Species
#### 3.2.3 Molecular Detection of Pathogens  
Molecular detection of pathogens such as _Rickettsia_ spp., _Borrelia_ spp., _Bartonella_ spp., _Anoplasmataceae_, _Coxiella burnetii_, _Leishmania_ spp. and _Leptospira_ spp. was performed on cuts of spleen and in ectoparasites collected from small mammals in Faladje and Bougouni. The qPCR method was used, and findings have been detailed in a previous publication [16]. The distribution and prevalence of pathogens detected in small mammals was quite different between the two sites. The overall prevalence of _Bartonella_ DNA in small mammals was 23/130 (17.7%): 7/44 _M. erythroleucus_ (16%), 5/14 _M. natalensis_ (35.7%), 1/8 _P. daltoni_ (12.5%) and 1/20 _C. olivieri_ (20%) were infected by _Bartonella_ spp. in Faladje, but _M. erythroleucus_ was commonly infected by _Bartonella_ spp. in Bougouni (5/14) (35.7%) compared to Faladje  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Species Captured** & **Family** & **Subfamily** & **Number of Specimens** & **Number of Ep (n = 48)** & **Total** \\ \hline Total & & & 75/123 (60.97\%) & 48/123 (39.02\%) & \\ _p_-value & & & Binomial test; & \\
95\% confidence interval & & & \(p\) = 0.018 \(<\) 0.05 & \\ (95\% CI) & & & 95\% CI [51.8–69.6] & 95\% CI [30.3–48.2] & 123 \\ Shannon index (H) & & & 0.54 & 0.59 & \\ Equi-distribution index (E) & & & 0.26 & 0.28 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Sex** & **Faladje (n = 75)** & **Bougouni (n = 48)** \\ \hline Male (M) & 38 (50.7\%) & 28 (58.3\%) \\ Female (F) & 37 (49.3\%) & 20 (41.7\%) \\ \hline Total & 75 (100) & 48 (100) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sexes of small rodent and insectivore specimens autopsied in Faladjé and Bougouni in Mali, December 2016.  
(4/44) (16%); similarly, _M. natalensis_ was more frequently infected by _Bartonella_ spp. (35.7%) in Faladjie than _M. erythroleucus_ (1/12) (8%) in Bougouni. _R. rattus_ captured in Bougouni were not infected any pathogens compared to _R. norvegicus_, which were more frequently infected with _Bartonella_ spp. (3/6) (30%) in Bamako. _Coxiella burnetii_ was detected only in Faladjie, where _M. erythroleucus_ (2/44) (4.5%) and _P. daltoni_ (1/8) (12.5%) were infected. These two species were trapped around human dwellings in Faladjie. All small mammal spleen cuts tested for _Leishmani_ spp., _Leptospira_ spp. and _Borrelia_ spp. DNA were negative.
# A Screw-Axis Approach to the Stability of Two-Wheeled Vehicles
## 4 Weave Analysis
### Top-View  
Figure 3 shows the trajectory of the velocity centre (centrode) during the weave oscillation, when the transverse plane considered is through the saddle (which in this case is at 0.8 m from the road on static condition). The pattern changes with the speed: At 10 m/s the trajectories cross between the handlebar and the centre of mass at 0.795 m from the rear contact point, while at 30 m/s and 50 m/s the cross point shifts towards the rear contact point at 0.633 m and 0.623 m, respectively. Following the nomenclature introduced in [24], such a condition is called over-steering weave (centrode intersection ahead of the centre of mass, which is at 0.6 m from the rear contact point on static condition).  
However, the centrode intersection changes when considering different transverse planes. Another option is to choose a plane through the centre of mass (which in this case is at 0.58 m from the road on static condition), see Figure 4. In this case the intersections exhibit the same behaviour mentioned above, moving from 0.709 m at 10 m/s to 0.615-0.613 m at 30 and 50 m/s, respectively, getting closer to the centre of mass while remaining still ahead of it (over-steering weave).  
Yet another option is to consider the projection on the road plane. In this case the results are those in Figure 5: The intersection appears behind the centre of mass, moving closer to it from 0.479 m to 0.568-0.587 m at 10, 30 and 50 m/s, respectively--following the nomenclature introduced in [24] this weak is called under-steering.  
The saddle plane may be a good choice if one is willing to associate the shape of the mode to the rider's feeling and perceived stability. On the other hand, the centre of mass plane has been apparently employed in [24], while the road plane is the choice in [21].  
Figure 3: Velocity centres of weave in a transverse plane through the saddle point.
## 4 Simulation Results and Discussions  
To demonstrate the efficiency of our optimization strategy, seven different designs were compared. Designs 1 and 2 were optimized for single-mode PUF and single-mode SRAM with the parameters of \(\mathbf{X}\) set to the minimum and maximum values, respectively. The transistors of Design 3 were sized for the lowest \(\mathbf{Pr}(\text{RF,WF})\) under the constraints of reliability (\(1-\text{BER}\)), area and leakage without applying any circuit-level techniques. Design 4 uses WLM to improve the cell stability, with \(V_{WL}=0.8\) V in read operation and \(V_{WL}=0.9\) V in write operation. Design 5 uses DVS, with \(V_{DD}=1\) V in read operation and \(V_{DD}=0.8\) V in write operation. Design 6 uses NBL, with bit-line voltage \(-0.1\) V applied in write operation. Design 7 is a ring oscillator (RO) PUF used for dual-mode application, i.e., as a PUF and a clock. The designs were simulated by HSPICE with CMOS \(45\) nm bulk Predictive Technology Model. The maximum standard deviation of \(V_{th}\) and \(\Lambda\) are assumed to be \(15\%\) of its nominal value and \(100\) nm, respectively. The channel length is set to \(45\) nm for all transistors. The ECC used is BCH-[127; 15; 27][22]. The following design constraints were set: \(\text{BER}_{\text{max}}=10^{-6}\), \(\text{area}_{\text{cell}}_{\text{max}}=1\)\(\mu m^{2}\), \(\text{leakage}_{\text{cell}}_{\text{max}}=50\)\(nA\) and \(\text{power}_{\text{cell}}_{\text{max}}=200\)\(\mu W\).  
The results are tabulated in Table 2. Design 1 has a low BER, which is good for PUF, but bad for memory mode due to its high \(\mathbf{Pr}(\text{RF,WF})\). Comparing to Design 1, Design 2 has a lower \(\mathbf{Pr}(\text{RF,WF})\) and a higher BER, which makes it a better choice for memory but a poor choice for PUF. Design 3 has a lower \(\mathbf{Pr}(\text{RF,WF})\) than Design 1. Design 4 to Design 6 meet the power constraint, but Design 5 has the lowest failure rate, which is due to the fact that both \(\mathbf{Pr}(\text{RF})\) and \(\mathbf{Pr}(\text{WF})\) are high, and they can only be reduced simultaneously by DVS. However, if we look at \(\mathbf{Pr}(\text{RF})\) and \(\mathbf{Pr}(\text{WF})\) separately, WLM is the most efficient for \(\mathbf{Pr}(\text{RF})\) reduction and NBL is the most efficient for \(\mathbf{Pr}(\text{WF})\) reduction, which attests to our postulation in Section 3.2. Compared with the dual-mode SPUF, RO PUF inDesign 7 has a lower BER but a much higher functional failure rate. The optimal design (Design 5) reduces the failure rate in memory mode from the baseline design (Design 1) by 75%, while reducing the leakage by 21.4% and keeping the BER at an acceptable range of \(\leq 10^{-6}\) in PUF mode at the cost of a reasonably small area and power overheads of \(\sim\) 8.9% and \(\sim\) 2.1%, respectively. It is worth noting that the bit error rate and code rate can both be improved by concatenating two ECCs at the expense of decoder complexity [22; 23].
# A Low-Cost IoT-Based Bidirectional Torque Measurement System with Strain Gauge Technology
## 1 Introduction
There is a clear need for practical, affordable, and statistically robust systems capable of accurate bidirectional (clockwise and counterclockwise) torque measurement, with transparent calibration and error compensation procedures. This paper addresses these limitations by presenting the practical development, assembly, and calibration of a wireless, IoT-enabled torque measurement system. The system is designed for straightforward installation on rotating shafts. Calibration is performed using a certified dynamometric wrench, with the relationship between sensor output and applied torque established through high-order polynomial regression analysis to accurately capture the sensor's nonlinear response. The calibration procedure yields a set of polynomial coefficients that define the conversion from digital output to applied torque across the full measurement range. Error metrics such as mean absolute error and standard deviation are reported to ensure transparency and reproducibility. The methodology is specifically designed to be transparent and verifiable, addressing the need for practical calibration in reversible and oscillatory load environments. The scope of this paper is the development of a cost-effective, wireless, and bidirectional torque measurement system. The novelty of this work lies in the integration of strain gauges, open-source electronics, and robust calibration within a single, IoT-enabled platform. This approach provides a verifiable pathway for implementing reliable wireless torque measurement in automotive, industrial, and research contexts, bridging the gap between laboratory-grade accuracy and practical field deployment.
# Incentive Price-Based Demand Response in Active Distribution Grids
## 2 Demand Response Methods for Control of DERs: A Review and Comparison
### Centralized Demand Response Control of DERs  
Figure 1 shows a demand response method based on solving a centralized optimization problem [13]. This methodology employs a central controller called low-voltage grid controller (LVGC) located either at the DSO control center or at the secondary substation. This controller computes power setpoints to each DER at every time-step (typically 10-15 min) based on the received feedback from the grid, states of the DERs (e.g., the state of charge of the BESS) and the forecasted power values. Each DER has a local controller called DER controller, which receives the power setpoints from the LVGC and does the local control of the DER. The distribution system state estimation (DSSE) algorithm takes fewer measurements from the grid at critical nodes, and uses pseudo-measurements at rest of the nodes to estimate the grid states i.e., the node voltages as well as the powers at all nodes [19; 20]. A forecasting algorithm provides hourly forecasted values of the PV generation and the load consumption at all nodes for the next few hours. The typical control objectives of the centralized optimization problem are minimizing the grid power losses, voltage fluctuations over time, maximizing the self-power consumption of PV power generation etc. [21].  
Figure 1: Schematic diagram of centralized control for demand response of distributed energy resources (DERs) based on [13].  
As seen in Figure 1, the centralized controller needs to know the grid states (node voltages and powers), and internal states of the DERs (e.g., state-of-charges of the BESS and room temperatures of the home heating systems controlled by heat pumps). This method may be useful for demand response applications in small and medium-scale distribution grids with small number of DERs.  
The demerits of the above centralized control method are as follows.  
* Data privacy of the customers is not ensured as the states of DERs contain sensitive information.
* The centralized control method will have issues with respect to scalability and reliability and therefore cannot be applied to larger distribution grids with thousands of DERs.
* The customers are not empowered to be active and dynamic when they participate in demand response programs.
* The communication overhead will be high as this method involves communication between DERs and the DSO control center.
_Article_  
**Strain Influence Factor Charts for Settlement**  
**Evaluation of Spread Foundations based on the Stress-Strain Method**  
**[PERSON](r)**  
Department of Civil Engineering and Geomatics, Cyprus University of Technology, 2-8 Saripolou str,  
3603 Limassol, Cyprus; [EMAIL_ADDRESS]  
Received: 10 May 2020; Accepted: 21 May 2020; Published: 31 May 2020  
**Featured Application: This study is a practical tool for Civil Engineers for the settlement evaluation of spread foundations using the stress-strain method.**  
**Abstract: In this paper, the stress-strain method for the elastic settlement analysis of shallow foundations is revisited, offering a great number of strain influence factor charts covering the most common cases met in civil engineering practice. The calculation of settlement based on strain influence factors has the advantage of considering soil elastic moduli values rapidly varying with depth, such as those often obtained in practice using continuous probing tests, e.g., the Cone Penetration Test (CPT) and Standard Penetration Test (SPT). It also offers the advantage of the convenient calculation of the correction factor for future water table rise into the influence depth of footing. As is known, when the water table rises into the influence zone of footing, it reduces the soil stiffness and thus additional settlement is induced. The proposed strain influence factors refer to flexible circular footings (at distances 0, \(R\)/3, \(2R\)/3 and \(R\) from the center; \(R\) is the radius of footing), rigid circular footings, flexible rectangular footings (at the center and corner), triangular embankment loading of width \(B\) and length \(L\) (\(I\)/\(B\) = 1, 2, 3, 4, 5 and 10) and trapezoidal embankment loading of infinite length and various widths. The strain influence factor values are given for Poisson's ratio value of soil, ranging from 0 to 0.5 with 0.1 interval. The compatibility of the so-called \"characteristic point\" of flexible footings with the stress-strain method is also investigated; the settlement under this point is considered to be the same as the uniform settlement of the respective rigid footing. The analysis showed that, despite the effectiveness of the \"characteristic point\" concept in homogenous soils, the method in question is not suitable for non-homogenous soils, as it largely overestimates settlement at shallow depths (for \(z\)/\(B\) < 0.35) and underestimates it at greater depths (for \(z\)/\(B\) > 0.35; \(z\) is the depth below the footing and \(B\) is the footing width).**  
**Keywords: strain influence factor; immediate settlement analysis; elastic settlements; Schmertmann's method; Cone Penetration Test; water table correction factor**
# LightVSR: A Lightweight Video Super-Resolution Model with Multi-Scale Feature Aggregation
## 3 Feature Aggregation Module
### Multi-Input Attention  
In video super-resolution, one of the feature fusion issues is the uneven distribution of information across different channels of feature maps. Some channels with critical information (e.g., complex details, dynamic objects) contribute more, while others with non-critical information (e.g., background, static objects) contribute less to the overall visual quality. This imbalance in information density may affect the quality of feature fusion and the subsequent reconstruction of high-resolution frames. To tackle this issue, we designed a multi-input attention mechanism to assign relative priority to each channel based on its contribution. By dynamically assigning higher weights to channels with complex textures or significant motion, the module can perform efficient feature fusion and thus benefit detailed reconstruction in key areas.  
As shown in Figure 4, the multi-input attention mechanism dynamically integrates multiple feature tensors by assigning adaptive weights to each channel. For input tensor \(T_{i}(i=1,\ldots,n)\), where each tensor \(T_{i}\) has the shape \(B\times C\times H\times W\) (batch size, channels, height, width), we first fuse all tensor via an element-wise summation:  
\[T=\sum_{k=1}^{n}T_{k} \tag{8}\]  
and squeeze the tensor along the channel dimension:  
\[X_{c}=\frac{1}{H\times W}\sum_{i=1}^{H}\sum_{j=1}^{W}T_{c}(i,j) \tag{9}\]  
where \(T_{c}\) represent tensor on channel \(c\). Then we apply the SoftMax function for all \(X\) to obtain the weight \(S_{i}\) of \(X_{i}\).  
\[S_{i}=\frac{exp(X_{i})}{\Sigma_{j=1}^{c}exp(X_{j})}\ \ for\ i=1,2,\ldots,C \tag{10}\]  
where \(C\) is the number of channels. The resulting weight vector \(S=[S_{1},\,S_{2},\,\ldots,\,S_{C}]\) is used to re-weigh each input channel such that the model can more effectively focus on key information and reduce the weight of channels with unimportant information. The final weighted output tensor \(F\) is computed as:  
\[F=\sum_{k=1}^{n}\sum_{j=1}^{c}T_{j}^{k}S_{j}^{k} \tag{11}\]  
Figure 3: Architecture of HT-Block.
## 2 Problem Description
### Instance of the Problem  
An instance of the problem is made up of two parts, a Directed Acyclic Graph (DAG) and the computation cost of the tasks in every machine. We represent the set of tasks of a parallel computing program and their precedencies as a DAG. Therefore, the parallel program is represented as the graph \(G=(T,C)\) where \(T\) is the set of tasks (vertices) and \(C\) is the set of communication costs between tasks (edges) (see Figure 1). The complete instance of the problem is formed by \(G\) and the computational costs \(P_{i,j}\) of each task \(t_{i}\) in every machine \(m_{j}\) (see Table 1).  
Any task \(t_{i}\) cannot be initiated until its precedent tasks \(t_{j}\in T|(t_{j},t_{i})\in C\) finalize their executions and communications \((C_{j,i})\). However, when any pair of tasks are scheduled in the same machine, the actual communication cost \(C^{\prime}_{j,i}\) between them is depreciated.
## 4 Results and Analysis
### P2P Traded Energy during Solar Hours  
Figure 13 shows the amount of P2P energy traded over the solar hours. From the figure, it is evident that the P2P energy traded between the participants increased for the hours where there was more surplus PV power for the prosumers. For the solar hours where there was less PV energy available, the P2P energy traded also decreased. The amount of traded energy for the ZI strategy was comparatively higher when compared to the GT method, as is explained above.
## 2 Materials and Methods  
In our proof of concept approach, we have applied a DUO MLX device (see Figure 1b) equipped with a stereovision camera, a three-axis electronic gyroscope, three-axis acceleration sensor, a thermometer, and a magnetometer [28]. The device is of small form factor: \(52\times 25\times 13\) mm and weighs 12.5 g. The device is mounted on the user's forehead (see Figure 1a). The study was conducted with 65 persons. The participants were mainly second year university students, 44 of whom were women. The trials were approved by the bioethics commission of the Medical University of Lodz (No. RNN/261/16/KE). All the trial participants were informed about the purpose of the study, the materials used and their role in the trial sessions. After fixing the DUO MLX device on their forehead, the users were asked to perform rehearsal head movements and were instructed to proceed with the movements just within a comfort zone of their head positions. During collection of the data, the participants remained in a sitting position. Each participant was sitting straight in a chair and did not change position during the experiment and performed only the given motions (yaw, pitch, roll, and immobility). Each user had the DUO MLX device mounted rigidly on their forehead. The participant did not touch the DUO MLX device or change its position on the forehead during the experiment.  
The aim of the study was to automatically classify three basic head gestures: _roll_ (head movement \"sideways\"), _pitch_ (head movement \"up-down\") and _yaw_ (head movement \"left-right\"), and _immobility_ (head at rest). A single movement cycle lasted 2.5 s on average with 1.2 s standard deviation of the movement period. The recordings of test and learning datasets from one individual took approx. 4 min each.  
Sample plots of the signals recorded by the acceleration sensor and the gyroscope are shown in Figure 2. The recorded accelerations \(a_{x}\), \(a_{y}\), and \(a_{z}\) along axes \(Ox\), \(Oy\), and \(Oz\) respectively are plotted in  
Figure 1: (**a**) Placement of the inertial measurement unit on the user’s head; (**b**) DUO MLX Stereo System equipped with inertial sensors with marked coordinate axes \(Ox\), \(Oy\), and \(Oz\), and angular velocities \(\omega_{\alpha}\), \(\omega_{\beta}\), and \(\omega_{\gamma}\).  
Figure 2a and angular velocities \(\omega_{\alpha}\), \(\omega_{\beta}\), and \(\omega_{\gamma}\) (as shown in Figure 1b) recorded from the gyroscope are plotted in Figure 2b. All the signals are recorded at a sampling rate of 100 Hz.
## 4 Experiments and Results
### Evaluation Metrics  
Our test set was evaluated by the standard Dice coefficient as the evaluation metric. The overlap of the algorithm segmentation results and the ground truth labels was measured using the Dice score. For each image, we calculated the prediction result of the OD and OC Dice scores.  
\[Dice=\frac{2|X\cap Y|}{|X|+|Y|}, \tag{6}\]  
where \(X\) is the ground truth and \(Y\) is the prediction result.
# A Novel Evolutionary Algorithm for Designing Robust Analog Filters
## 1 Introduction
In natural evolution, the evolution of the functional structures of living organisms contributes significantly to their robustness with respect to the noisy environment. Functional characteristics such as modularity, redundancy, and negative feedback control are all typical features of living systems in response to survival requirements for robustness. Our hypothesis here is that control factors (design variables) as used in the robust design framework in [8] should not be limited to changing the dimensions (or sizing) and other parameters of the system, and that the system's robustness should be considered when designing or evolving the functional or topological structure of the system. As any given function of a dynamic system can be implemented in various ways, we believe that the topological or functional design in the conceptual design phase may have a significant role in determining the ultimate robustness of the design solutions achievable through applications of traditional robust design procedures. There is already a body of research on how the structure of a system affects its functional robustness. [PERSON] et al. [9] analyzed topological patterns in electric circuits and their relationship with the properties of the system behavior. [PERSON] and [PERSON] [10; 11], among others, discussed how the coupling structure of the system may affect robust parameter design. Robust design for truss topology has also been studied in [12]. However, a systematic methodology and investigation of robust design of dynamic systems based on a topologically open-ended search by genetic programming is still not available. To examine the role of topology search in designing robust systems, design problems of two analog filters, i.e., a lowpass filter and a highpass filter, are synthesized using genetic programming. For each synthesis problem, three experiments are conducted: (a) evolutionary synthesis using GP without considering robustness criteria, (b) improvement in robustness of these evolved filters by tuning their parameters using a genetic algorithm (GA), and (c) evolution of robust filters (topological structure and parameters) using GP with a robustness criterion in the fitness function. These filter design problems are selected as they are perhaps the most popular problems in evolutionary synthesis research by either GA or GP [13; 14; 15]. The rest of the paper is organized as follows. Section 2 presents a short survey of applications of evolutionary algorithms in robust design. Section 3 introduces our GPRD (GP-based robust design) methodology, which exploits GP and bond graphs for automated synthesis of robust systems. Section 4 compares the experimental results of these approaches. Finally, conclusions and future research are highlighted in Section 5.