esa-sceva/satcom-chunk-collection huggingface dataset:
# Scheduling of Industrial Control Traffic for Dynamic RAN Slicing with Distributed Massive MIMO +
## Appendix A An Example  
To illustrate how the constraints (2m)-(2o) work, consider the case \(S=5\) and a binary vector \(x=(x^{1},x^{2},x^{3},x^{4},x^{5})\), for which we wish to find the index \(T^{\prime}\) of its last entry equal to 1. Then, constraints (2m) and (2n) are as follows:  
\[z^{1}\geq x^{1}\] \[z^{1},z^{2}\geq x^{2}\] \[z^{1},z^{2},z^{3}\geq x^{3}\] \[z^{1},z^{2},z^{3},z^{4}\geq x^{4}\] \[z^{1},z^{2},z^{3},z^{4},z^{5}\geq x^{5}\] \[z^{1}\leq x^{1}+x^{2}+x^{3}+x^{4}+x^{5}\] \[z^{2}\leq x^{2}+x^{3}+x^{4}+x^{5}\] \[z^{3}\leq x^{3}+x^{4}+x^{5}\] \[z^{4}\leq x^{4}+x^{5}\] \[z^{5}\leq x^{5},\]  
where \(z=(z^{1},z^{2},z^{3},z^{4},z^{5})\) are auxiliary binary variables.  
Now, let \(x=(x^{1},x^{2},x^{3},x^{4},x^{5})=(0,1,0,1,0)\). For this particular vector, \(x\), the above inequalities are:  
\[z^{1}\geq 0\] \[z^{1},z^{2}\geq 1\] \[z^{1},z^{2},z^{3}\geq 0\] \[z^{1},z^{2},z^{3},z^{4}\geq 1\] \[z^{1},z^{2},z^{3},z^{4},z^{5}\geq 0\] \[z^{1}\leq 2\] \[z^{2}\leq 2\] \[z^{3}\leq 1\] \[z^{4}\leq 1\] \[z^{5}\leq 0.\]  
The unique binary solution is \(z^{1}=z^{2}=z^{3}=z^{4}=1\), and \(z^{5}=0\). Thus, according to Equation (2o), \(T^{\prime}=z^{1}+z^{2}+z^{3}+z^{4}+z^{5}=4\), as required.  
In a similar way, we can illustrate the correctness of the constraints (2p)-(2r) determining the index of the first entry in \(x\) equal to 1.
## 4 Discussion
### Limitations  
No attempt was made to identify or translate non-English-language publications, which may have limited the inclusion of some relevant studies. There is a possibility of publication bias, as the review included only studies published since 2012 and those which focused on TBI patients, which could act as a possible restriction on the number of studies reviewed. The variability in the sizes of the datasets and approaches used to train and test the models, together with the fact that one of the studies contained other diagnoses alongside TBI, may have affected the outcomes of the review. The literature has shown the prevalence and effect of TBI on patients and on society. As the studies in this review describe, it seems reasonable to suggest that the ability to successfully predict ICP crises in TBI patients and subsequent timely interventions from healthcare professionals may enhance patient outcomes. Although this review identified GP, logistic regression and LSTM machine learning models as the most effective techniques for predicting ICP in TBI patients, further research is required, to collect more data for the use of similar predictive models in a clinical setting. The heterogeneity of the methodologies of the different studies and predictive models did not allow for the undertaking of a meta-analysis and, thereby, for a definitive conclusion.
# Limit Equilibrium Models for Tunnel Face Stability in Composite Soft-Hard Strata
###### Abstract  
The tunnel face stability in composite strata, commonly referred to as the soft upper and hard lower condition, is a critical challenge in tunnel construction. The soft-hard ratio (SA) strongly influences the limit support pressure as well as the failure mechanism experienced by a tunnel face. This study focused on the Xingye Tunnel project in the Xiangzhou District of Zhuhai City. By conducting numerical simulations, the impact of different SAs on the limit support pressure was investigated. Furthermore, a limit equilibrium model was established on the basis of the analysis of the results of numerical simulation. The findings were then compared and analyzed alongside those of relevant theoretical models. In the event of tunnel face instability of composite strata, the deformation tends to be concentrated mainly in the soft soil layer, with less noticeable deformation in the hard rock layer. The investigation of different SAs revealed a linear decrease in the limit support pressure ratio of the tunnel face in composite strata as SA decreases. The self-stability of the tunnel face was observed when SA \(\leq\) 0.125. Moreover, the limit support pressure ratio predicted by the truncated log-spiral model (TLSM) exhibited a higher degree of agreement with the results of numerical simulation than those of other relevant models. The superiority of TLSM was mainly demonstrated in the range of SA = 0.25 to 1.0.  
composite strata; soft upper-hard lower; super-large diameter; tunnel face; limit equilibrium method
# Influence of haptic Sensory Input through Different Kinds of Clothing on Gait Performance
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & \multicolumn{2}{c}{**DC**} & \multicolumn{2}{c}{**TS**} & \multicolumn{2}{c}{**ANOVA**} & \multicolumn{2}{c}{**Effect Size**} \\ \hline  & **Pre** & **Post** & **Pre** & **Post** & **Pre-Post** & **Dec-TS** & **Interaction** & **ANOVA** & **DC** & **TS** \\  & **(HT)** & **(DC)** & **(HT)** & **(TS)** & **Pre-Post** & **Dec-TS** & **Interaction** & **(Interaction)** & **(Pre-Post)** & **(Pre-Post)** \\ \hline \multicolumn{10}{c}{Normal gait test} \\
**Gait time (s)** & \(12.50\pm 0.85\) & \(11.34\pm 0.87\,^{*}\) & \(11.66\pm 0.79\) & \(12.42\pm 10.6\) & N.S. & \(p=0.02\,^{\dagger}\) & \(F=0.56\) & \(d=0.39\) & \(d=0.24\) \\
**Number of steps** & \(18.75\pm 0.84\) & \(18.08\pm 0.85\) & \(18.25\pm 0.85\) & \(19.00\pm 1.07\) & N.S. & \(p=0.04\) & \(F=0.48\) & \(d=0.23\) & \(d=0.22\) \\
**Error (m)** & \(0.56\pm 0.20\) & \(0.73\pm 0.15\) & \(0.92\pm 0.26\) & \(1.11\pm 0.22\) & N.S. & N.S. & \(N.S.\) & \(F=0.01\) & \(d=0.27\) & \(d=0.23\) \\ \hline \multicolumn{10}{c}{Tandem gait test} \\
**Gait time (s)** & \(17.21\pm 1.51\) & \(14.09\pm 0.76\,^{*}\) & \(15.39\pm 0.89\) & \(16.23\pm 1.00\) & N.S. & N.S. & \(p=0.02\,^{\dagger}\) & \(F=0.53\) & \(d=0.80\) & \(d=0.26\) \\
**Number of** & \(0.58\pm 0.22\) & \(0.08\pm 0.08\,^{*}\) & \(0.42\pm 0.14\) & \(0.67\pm 0.18\) & N.S. & N.S. & \(p<0.01\,^{\dagger}\) & \(F=0.60\) & \(d=0.88\) & \(d=0.45\) \\
**Energy (m)** & \(0.58\pm 0.12\) & \(0.41\pm 0.13\) & \(0.37\pm 0.08\) & \(0.48\pm 0.09\) & N.S. & N.S. & \(F=0.31\) & \(d=0.39\) & \(d=0.37\) \\ \hline \end{tabular}  
* N.S: Not Significant, \({}^{*}\) vs. \({}^{*}\)Pre-, \(p<0.05\) (Holm’s test), \({}^{\dagger}\) Statistical power (\(1-\beta\)) \(\geq 0.80\).  
\end{table} 
Table 1: Mean value of each variable during normal and tandem gait tests. Values are expressed as mean \(\pm\) standard error of the mean.
# A Full Technological Traceability System for Extra Virgin Olive Oil
## 3 Results and Discussion  
### Technological Traceability System  
The investigations conducted in the olive grove, after 1 year from their application, showed that 31 tags positioned on the 33 olive trees belonging to the three cultivars (Carboncella, Frantoio and Leccino) were correctly read by the dedicated app and therefore proved to be functional, recording all the necessary information. Only two labels could not be read because of the nail due to the fact that they had been repaired, removed and the label was peeling off the bark of the tree. Each tag uniquely associated with a tree was associated with crates. For each plant, the following information relating to each crate was added: weight of the crates, maturity of the olives (high, medium, low), defects of the olives (high, medium, low), other information (notes) and type of collection. For the three cultivars (Carboncella, Frantoio and Leccino), 641.5 kg, 341 kg and 134 kg of olives were collected, respectively. The weights of the crates of olives were recorded in the mill on the day of collection; the following day, the olives of three cultivars (Carboncella, Frantoio and Leccino) were pressed and the yield was calculated (17.20 kg, 16.27 kg and 20 kg, respectively). Subsequently, nutritional information on the oil was carried out by measuring the fats (saturated, monounsaturated, polyunsaturated), carbohydrates, proteins, salt and vitamin E. The information acquired was successfully recorded by TTS infotracing. At that point, through the QR code tag printed on the external label of the 100 EVOO bottle and protected by a \"scratch and win\" system, consumers were able to access information regarding the entire EVOO supply chain examined. This system is a gamification method that protects the QR code to allow the consumer who buys the product to get the prize. The infotracing platform is implemented with blockchain. Once the consumer purchases the product, scratching the label and framing the QR code with a smartphone, he or she receives, together with the product information, a digital token of \(\copyright\)0.05, produced by the blockchain itself, which can be spent on the platform of reference. The blockchain system allows one to ensure that the information entered along the entire supply chain is tamper-proof. In this case, innovation is represented not only by the introduction of the blockchain, but also by the combined use of the \"scratch and win\" system to protect the consumer. The system includes standard motivational models and technological models within the reach of the consumer [6].
# SOC Estimation with an Adaptive Unscented Kalman Filter Based on Model Parameter Optimization
## 2 Establishment of Battery Model and Parameter Identification
### Identification and Verification of Battery Model Parameters  
In the process of establishing the battery model, it is necessary to use the approximate linear relationship between the OCV and the SOC. In this section, based on the OCV-SOC relationship curve, the parameters of the second-order RC model are identified, and then the accuracy of the model is verified.
Besides, they also investigated the role that local ABH cell modes/modal density played in vibration suppression. [PERSON] et al. [15] investigated the influence of structural parameters on energy focalization positions of the bending wave by using FEM. [PERSON] et al. [16] established a FE model of a car main floor embedded with 10 ABHs, and its vibration response under a wind load excitation was studied. Results of simulation showed that noise reduction was found in the whole frequency range above 200 Hz. In these studies, most researchers used radiated sound power or vibration acceleration to express the energy focalization characteristics of ABH structures. However, the visualization research on the vibration energy propagation process in ABH areas is limited still. In addition, the characteristics of energy propagation in a plate embedded with a  
Figure 1: Plate with an ideal parabolically tapered edge. two-dimensional ABH array have not been investigated systematically. In general, the systematic theory and method of two-dimensional ABH array design have not yet been formed, and the research on low-frequency vibration reduction performance of the ABH array is not enough. Therefore, it is difficult to design body panels by ABH theory which can achieve both vibration and noise control and be lightweight. Aiming at these problems, in this paper, a thin-plate embedded with an array of five ABHs is designed, and its energy propagation and sound radiation performance due to concentrated load is examined using FEM. The structural intensity is computed in order to quantitatively evaluate the plate structure's energy focalization performance. The overall goal of this work was to develop detailed results for the energy propagation and focalization effects of the ABH plate, to investigate the energy distribution of the high-energy density areas of the individual ABH cell of the array, to study the influence of damping materials and plate boundary conditions on energy propagation, and to explore the sound radiation performance of the plate embedded with the ABH array. This work has potential for solving the contradiction between vibration suppression and automobile lightweight, which can provide a reference for body panel design.
## 4 Simulation Results and Discussion
In case 5, due to the unbalanced supply and unbalanced load with a wind speed of 10m/s, the load current was sinusoidal and unbalanced, as shown in Figure 18(a) with a THD of 9.26% and PF of 0.6382. The proposed hybrid controller effectively reduces THD from 9.26% to 2.30% and boosts PF from 0.6382 to 0.999. The proposed method was also able to maintain voltage across the DC-link effectively during the wind velocity variation, as shown in Figure 18(b). The THD comparison for all the test cases is given in Figure 18. However, from Figure 18(c), it is clearly observed that the proposed method boosts up the PF to a much higher value almost to unity when compared to other control techniques for all the test studies efficiently. From the waveforms 15-29, it is clearly observed that the time required to reach the stable DC-link voltage without peak overshoot is 0.05 sec, which is much less than conventional methods. Figure 18(c) exhibits the superior performance of the proposed method in maintaining constant DC-Link voltage during wind velocity variations. Figure 18(d) gives the THD spectrum of the proposed method for all the test cases for validation.
## 6 Results
### Thermal Comfort  
Maintaining optimal environmental conditions within indoor spaces is imperative for ensuring occupant well-being. Accurate monitoring of temperature, relative humidity, and room occupancy levels is pivotal for thermal load forecasting and management, a fact that is particularly salient in the absence of a Building Management System (BMS). In such cases, the transition between heating and cooling operations is neither instantaneous nor uniform, necessitating strategic planning of heat loads by operators. This challenge is further compounded in older buildings, which were not originally designed to accommodate HVAC or air conditioning systems. In office environments, a moderate level of occupancy is expected. For the thermal comfort analysis, the following assumptions were made:  
* Temperature: indoor temperatures should be maintained between 18 \({}^{\circ}\)C and 22 \({}^{\circ}\)C during winter and between 23 \({}^{\circ}\)C and 26 \({}^{\circ}\)C during summer. These temperature ranges have been determined to enhance comfort and efficiency while minimising health risks associated with extreme cold or heat exposure. The same ranges are considered optimal for most individuals based on the Perceived Mean Vote (PMV) model;
* Humidity: the relative humidity should be maintained between 50% and 80% to prevent excessively dry air, which can lead to respiratory issues, and to inhibit the growth of pathogens and allergens.  
The temperature and humidity trends for the period between 15 November 2024 and 31 January 2025 are shown in Figure 6.  
The continuous monitoring of temperature and humidity plays a critical role in energy consumption management. It is well established that air conditioning systems represent the primary energy load in many buildings, particularly in offices and schools. Implementing energy forecasting based on environmental monitoring is essential for several key reasons:  
* Thermal comfort and energy efficiency: maintaining optimal temperature and humidity levels is directly linked to occupant thermal comfort. By ensuring appropriate indoor environmental conditions that minimize the reliance on energy-intensive systems (such as heating and air conditioning units), significant energy savings can be achieved.
* Data-driven operational decisions: the collection of real-time environmental data from temperature and humidity sensors enables informed decision-making regard  
Figure 5: CO\({}_{2}\) levels over time.  
ing energy usage optimization. For instance, real-time monitoring allows for the dynamic adjustment of lighting and electronic devices, which contribute to indoor heat generation. By strategically managing these systems, comfortable conditions can be maintained while minimizing energy consumption.  
This data-driven approach enhances energy efficiency and sustainability, supporting the development of intelligent building management strategies.
## 4 Discussion
### Kinematics and Kinetics  
The results of this case study suggest that the SSS offer some changes in both kinetics and kinematics running-related parameters. One of the most interesting aspects to underline is the increasing contact time captured with the SSS, alongside an increased swing force rate and a reduced rate of vGRF (Figure 4), which possibly caused less stress on the foot musculoskeletal structures. The reduced contact times can seem counter-intuitive, considering that these shoes should help the athlete during the push-off phase of the run, as the carbon fiber plate enhances elastic force restitution [10]. Although contact time increased, no change was detected for step rate and length, which could be expected given the higher amount of time spent in contact with the ground. It can be reasonable to surmise that such modifications do not occur, as there are movement compensations elsewhere. Accordingly, an increasing swing force rate was captured. As a result, the athlete recovered from the longer times spent on the ground with a quicker swing phase.  
Even the vertical impact at the foot strike instant changed, and it was higher with the SSS. The increase in vertical impact could be related to two aspects: (1) the higher intrinsic rigidity of the SSS due to the carbon fiber plate [8]; (2) the concurrent change in the foot strike type [14], which showed a more horizontal position of the feet when the SSS were used. This value, however, does not fit with the athlete perception, because she felt less heavy on the ground. This is an important aspect to consider, especially given the high proficiency of the athlete analyzed in this study.  
The reduction in forefoot foot strike decreased Achilles tendon involvement as the first shock-absorbing joint, increasing the vertical impact acceleration [17]. This change should not be downplayed for two reasons. First, the foot strike patterns changed the vertical impact but not the horizontal braking; therefore, the amount of braking for each step was the same between the two shoes, meaning that no dissipation of energy was present. Second, the increase in vertical impact was only at the instant of the foot strike,  
Figure 6: Schematic representation of the medial and lateral percentage of load in the two different shoes. NSS led to an asymmetric loading behavior between the left and right feet, with a higher medial load percentage on the left foot, and a more distributed load percentage between the medial and lateral part of the right foot. By comparison, the SSS led to a more symmetric behavior between the left and right feet, increasing the medial load on the right foot, with similar values with respect to the left foot.  
but the acceleration was absorbed by the high-tech material of the shoes and not by the athlete's soft tissues because the vertical ground reaction force was lower with the SSS.
## 2 System Model of MC\(-\)ODBR\(-\)DCSK
### Transmitter Structure of MC\(-\)ODBR\(-\)DCSK
The signal format of the reference subcarrier formation \(l\) group is added to the data subcarrier, which is converted by IFFT, transmitted, and then sent to the wireless communication channel by parallel-serial conversion. By analyzing and combining the modulation block diagram, it can be found that the MC\(-\)ODBR\(-\)DCSK system can transmit \(2M\) bits of information in one symbol cycle. In order to show the signal forms in group lth more visually, a diagram of the MC\(-\)ODBR\(-\)DCSK signal structure is shown in Figure 4.  
The signal structure includes a reference signal and an information time signal. The reference signal is composed of two subcarriers generated by the chaotic generator \(s_{1}^{l}\) and the orthogonal generator \(s_{2}^{l}\) for addition and subtraction operations. Each group of information signals is divided into \(M\) subcarriers. Each subcarrier is composed of data \(a_{k}^{l}\) multiplied by the signal \(s_{1}^{l}\) generated by the chaotic generator, and data \(b_{k}^{l}\) multiplied by the orthogonal generator \(s_{2}^{l}\) and superimposed. In the MC\(-\)ODBR\(-\)DCSK system, M subcarriers are employed to transmit multiple data symbols in a parallel way so that a higher data rate could be achieved. As a T\(-\)R based system, MC\(-\)ODBR\(-\)DCSK transmits 2 chaotic signals as the reference signals in one group. Meanwhile, \(2M\) different data signals are transmitted over by \(M\) subcarriers. As a result, chaos synchronization circuit is not needed by this receiver since the reference signals. The chaotic signals, are available. In addition, wideband delay line is not needed either, since the reference and data-bearing signals are separated over orthogonal channels. For visualization, Figure 5 shows the format of the transmitted signal of the MC\(-\)ODBR\(-\)DCSK. Figure 4: The signal structure of MC\(-\)ODBR\(-\)DCSK. According to the signal structure shown in Figures 4 and 5, MC\(-\)ODBR\(-\)DCSK system transmits 2 times more bits than MC\(-\)DCSK system.
# Non-Iterative Cluster Routing: Analysis and Implementation Strategies
## 5 Results
### Classification across Various Viewpoints  
In this section, we evaluate the generalization capabilities of our proposed methods to novel viewpoints using the _smallNORB_ data set. To guarantee a fair comparison, we also retrained the cluster routing algorithm, applying the same data augmentation techniques as those used in our methods.  
The network was trained on the training set with varying capture angles or azimuths (300, 320, 340, 0, 20, 40). The test set comprised different instances in the same categories, with unseen angles ranging from 60 to 280. To evaluate the network's performance under varying elevation viewpoints, we selected three smaller elevations from the training set and tested the model on six larger elevations from the test set.  
From the novel azimuth viewpoint, our first variation achieved the highest accuracy of 85.27%. The multiple-channel version of the original cluster routing and our first variation demonstrated an advantage over the single-channel version. Regarding the novel elevation viewpoint, the single version of our second variation attained the highest accuracy of 84.44%, as depicted in Table 2. The performance of both variations was on par with the original cluster routing, maintaining robust generalization to novel viewpoints. In certain instances, they even exhibited a slight advantage.
## List of Abbreviations  
AV Autonomous Vehicle LiDAR Light Detection and Ranging MJPF Markov Jump Particle Filter DBN Dynamic Bayesian Network GNG Growing Neural Gas JPDA Joint Probabilistic Data Association NFF Null Force Filter KF Kalman Filter PF Particle Filter CLA Continuous-Level Anomaly KLDA Kullback-Leibler Divergence Anomaly KITTI Karlsruhe Institute of Technology and Toyota Technological Institute dataset MSE Mean Squared Error RMSE Root Mean Squared Error DBSCAN Density-Based Spatial Clustering of Applications with Noise CIFs Covariance Intersection Filters VAE Variational Autoencoder CMJPF Coupled Markov Jump Particle Filter