esa-sceva/satcom-chunk-collection huggingface dataset:
# Simplified Fly Tower Modeling for Preliminary Acoustic Predictions in Opera Houses
## 1 Introduction
Reverberation Time in opera houses is usually shorter than in concert halls; generally it is considered to be between 1.2 and 1.7 s (without set specifications), and other acoustic parameters, such as Strength (G) and Clarity C80 [17], must be checked to ensure the audience is enveloped or surrounded by the sound. To preserve the specific heritage of these Italian historical buildings, a standardized procedure for acoustic measurements in the main hall was defined by specialists for many years [18; 19; 20], while acoustic standards and regulations for buildings were identified [21; 22; 23]. Baroque theaters, in fact, constitute an architectural heritage of enormous importance for the whole of Italy, and in a broad sense, they represent a cultural heritage as a symbol of civilization [24] as well as a reference also for horseshoe shape and new opera houses [6]. More recently it was also found that it is better to locate the sound source in the front half of the stage near the proscenium, because when it is located at the back of the stage, it brings the risk of creating a too-reverberant sound field on the stage itself and thus an unfavorable effect due to the large difference in Reverberation Time appears between the stage and the auditorium [16]. This paper proposes a fly tower configuration and its modeling, to be used in an acoustic prediction analysis, in case of a restoration process or a new concept design phase, starting from the relationship of the stage house, stage set, and main hall [25; 26], in which the hall volume and the fly tower are considered as one single volume. Results and comparisons about the acoustic trend in a horseshoe-shaped theater, the \"Opera di Roma\", follow, in which a traditional set and a new digital Baroque scenography are installed, considering the suggested fly tower representation. Further research could focus on the adaptation of the new digital Baroque scenography in multipurpose auditoria [27; 28; 29], in modern churches [30], and in other spaces like circuses [31] used for classical and modern representations. In Section 2, the methodology of finding out a referring fly tower model is explained, starting from more common real stage house conditions; applicable limitations are discussed and processes and techniques to obtain the acoustic parameters are indicated. In Section 3, according to the explained methodology, the effects of the fly tower Depth and absorptive characteristics on the RT trend are investigated to better analyze the simplified model absorption characteristics, according to the considerations of Section 2, followed by investigations on the main acoustic parameters T30, C80, and G, referred to two compared scenarios: a traditional and a modern one, in the theater \"Opera di Roma\" (Italy). The first scenario, the Opera Semiramide, represents the traditional set, with reflecting surfaces on stage; the second one is the empty set for a new Baroque scenography, with most projection screens being transparent to sound. Conclusions follow.
## 3 Datasets and Methods  
### Datasets and Preprocessing  
Our research covers datasets in 5 languages: English, German, Dutch, Croatian, and Russian. The dictionaries created by interviewing informants were used, respectively, from the works [5; 7; 8; 9; 10].  
We study methods for the prediction of C/A ratings only for nouns. Therefore, original dictionaries were filtered: the English dictionary already had a POS tag, the Russian dictionary contained nouns only, and the remaining three dictionaries were filtered using the spacCy library ([[https://spacy.io](https://spacy.io)]([https://spacy.io](https://spacy.io)) (accessed on 4 May 2022)). Sizes of the resulting dictionaries are the following: English (13,802), German (3242), Dutch (15,242), Croatian (1616), and Russian (1000). When extrapolating ratings within one language, 90% of the available ratings are randomly selected as a training set and 10% as a test set.  
When transferring ratings from one language to another, the dictionary of the source language is used to train the model, while the dictionary of the target language is used for testing. Considering that all dictionaries have a different volume, the ratings were transferred in two versions--with complete dictionaries and with dictionaries of 1000 words. The most frequent nouns were included in the dictionary in the second case.
# Recommendation-Based Trust Evaluation Model for the Internet of Underwater Things
## 2 Background
### Trust Modelling  
Trust modelling involves constructing frameworks that evaluate the reliability of internal entities, aiming to identify and mitigate risks from insiders who might misuse their access or knowledge. This approach emphasizes continuous monitoring and analysis of behaviour patterns and interactions within the system, using both direct experiences and  
Figure 1: IoUT network structure.  
insights gathered from neighbours to prevent and respond to security breaches from within the network [12; 13; 14]. Trust in most IoT and MANET systems exists in the following forms:  
* Direct Trust: This formation of trust is based on direct interaction between two entities. The computed trust value will be a result of the observation and experience based on past interactions. Mathematically, it can be seen as: \[T_{ij}=f(X)\mapsto\{\text{observations, past/current experiences}\}\]
* Indirect Trust: This formation of trust relies on others' beliefs about an entity. A trust value will be computed based on a third-party opinion on a particular node. This can be carried out through a recommendation from either a mutual one-hop node neighbour or a multi-hop node recommendation using the trust's transitivity property. Mathematically, this can be represented as: \[T_{ij}=f(X)\mapsto\{\text{second-hand neighbour recommendations}\}\]  
A recommendation-based trust model is a system designed to evaluate the trustworthiness of entities within a network based primarily on the recommendations of others within that network. This concept is particularly useful in environments where direct interactions between entities are limited or non-existent, necessitating reliance on the feedback and experiences shared by others. The indirect aspect of trust refers to the trustworthiness derived not from direct interactions but from the recommendations provided by other entities in the network. Each entity in the network collects and aggregates recommendations about other entities [15].  
Ensuring valid recommendations is crucial for maintaining the integrity of the network and enabling entities to make informed decisions. The validity of recommendations poses significant challenges, especially in the complex nature of underwater networks where communication can be intermittent and unreliable. The open nature of IoUT, which lacks a comprehensive security infrastructure, increases the likelihood of attacks. One issue is that the network's sparsity renders most advanced recommendation systems ineffective in this context. This becomes particularly problematic when there is no current, relevant evidence, and due to the decaying nature of the trust, there will be high differences between the stored trust score and the recommendations. Additionally, there is a significant risk of mistakenly identifying honest nodes as dishonest due to link quality issues, leading to nodes being misjudged within the network and assigned incorrect trust levels due to communication misrepresentations.
# Reactive Balance Control for Legged Robots under Visco-Elastic Contacts
## 3 State of the Art  
### Inverse Dynamics with Rigid Contacts (TSID-Rigid)  
This section summarizes the classic approach for balancing a legged robot in rigid contact with the environment [4].  
The desired momentum rate of change is typically computed with a simple PD control law:  
\[\ddot{x}_{d}=\begin{bmatrix}m\ddot{c}_{d}\\ l_{d}\end{bmatrix}=\begin{bmatrix}mc^{*}+K_{d}^{com}\dot{e}_{c}+K_{p}^{com}e_{ c}\\ l^{*}+K_{d}^{am}\dot{e}_{l}+K_{p}^{am}e_{l}\end{bmatrix}, \tag{12}\]  
where \(e_{c}\) and \(e_{l}\) are the tracking error of the linear and angular part of the centroidal state, respectively. Often, the proportional part of the angular momentum feedback is neglected, but we use it here to ensure stability [6]. The contact forces and the robot accelerations are computed by solving the following least-squares problem:  
\[\begin{split}\underset{v,f}{\text{minimize}}&\|Af-\ddot{x}_{d}+b \|^{2}+w_{f}||f-f^{*}||^{2}+&w_{post}||\phi^{post}-\dot{v}||^{2}\\ \text{subject to}& M_{u}\dot{v}+h_{u}=J_{u}^{\top}f\\ & J\dot{v}+J\dot{v}=0\\ &|M_{u}\dot{v}+h_{a}-J_{a}^{\top}f|\leq\tau^{max}\\ & Bf\leq 0,\end{split} \tag{13}\]  
where \(w_{f}\in\mathbb{R}\) is the weight and \(f^{*}\in\mathbb{R}^{3\times}\) is the reference of the force regularization task, \(w_{post}\in\mathbb{R}\) is the weight of the postural task, the first constraint represents the centroidal dynamics, the second one is the rigid-contact constraint, the third constraint represents the joint torque limits, and the last one represents the friction cone limits. The reference postural task accelerations are:  
\[\vartheta^{post}=K_{p}^{post}e_{q}(q^{*},q)-K_{d}^{post}v, \tag{14}\]  
where \(K_{p}^{post},K_{d}^{post}\in\mathbb{R}^{(n+6)\times(n+6)}\) are positive-definite diagonal gain matrices, \(q^{*}\in SE(3)\times\mathbb{R}^{n}\) is a given reference posture, and \(e_{q}(q_{1},q_{2})\) is an error function mapping two configurations \(q_{1},q_{2}\in SE(3)\times\mathbb{R}^{n}\) to the log of their relative displacement. Once the optimal contact forces \(f_{d}\) and robot accelerations \(\hat{v}_{d}\) are found, we compute the desired joint torques using (10). Please note that this approach does not require (nor exploit) any force measurement.
## 2 Methodology
### Machine Learning Model
#### 2.3.7 Gaussian Process Regression  
Gaussian Process Regression (GPR) is a non-parametric model that provides notable benefits for predicting the performance of foamed concrete [94]. Unlike conventional parametric regression methods, GPR eliminates the need for specifying a fixed model or a predetermined number of parameters. As a result, it offers superior flexibility in reflecting the intricate relationship. In this study, the squared exponential kernel was adopted as the covariance function, enabling the model to better reflect subtle variations in the data while maintaining high predictive accuracy.
# Closed-Form Expressions of Upper Bound for Polarization-MDCSK System
## 4 Numerical Results and Discussions  
In this section, simulations are presented to verify the derived closed expression over multipath Rayleigh fading channel and AWGN. In all figures, \(SF\) denotes the spreading factor.  
A three-path fading channel with equal channel average power gain is considered, i.e., \(E[\alpha_{1}^{2}]=E[\alpha_{2}^{2}]=E[\alpha_{3}^{2}]=1/3\), and time delays \(\tau_{1}=0,\tau_{2}=2T_{c},\tau_{3}=5T_{c}\). Here, \(T_{c}\) is the sampling period of the chaotic signal \(\mathbf{c}_{x}\).  
Figure 2 shows the comparisons between the derived closed expression of BER and the simulated BER of the P-MDCSK system over AWGN and multipath fading channels. The modulation order is \(M=8\). The spreading factor is \(SF=64,128,256\) over AWGN channel and \(SF=64,128,256,512\) over multipath Rayleigh fading channels, respectively. The result shows that the closed expression is close to the simulations. The theoretical boundaries are roughly \(0.4\) dB away from the simulations when BER is \(10^{-4}\).  
The results of the simulation and the bounds analyzed in Section 3 for P-MDCSK are shown in Figure 3, with the parameters \(SF=128\), \(\theta=2\), \(L=3\) and \(M=4,8\). The upper bound is very close to the simulated BER curves, and the trend and gap with the tight upper bound of the derived closed-form expression are in the reasonable range zone. The theoretical upper and the closed-form boundaries are \(0.07\) dB and \(0.4\) dB away from the simulations when BER is \(10^{-4}\). The derived closed expression and upper bounds closely match the simulation curves and can constrain them from above and below. The simulation point at \(23\), \(24\), and \(25\) dB for \(4\)-P-MDCSK is slightly higher than other points. It is caused by the theory \(P_{MDCSK}\), whose theoretical derivation is an approximate expression, and the tendency of simulation and theory is similar in [12]. Thus, the BER performance of P-MDCSK over multipath Rayleigh fading channels can be predicted by upper and closed-form expressions.
# Metagenomic Analysis of Bacterial Diversity on Reusable Tourniquets in Hospital Environments
## 3 Results
### Identification of Pathogenic Bacteria
#### 3.2.2 Bacteria Pathogenic to Humans Found on Reusable Tourniquets  
On the surface of reusable tourniquets, our study found bacteria that are pathogenic to humans. On the surface of reusable tourniquets from the emergency department the following pathogenic bacteria were found: _Staphylococcus aureus_, _Staphylococcus hominitis_, _Enterococcus faecalis_, _Enterococcus hirae_, _Enterobacter quasihormuechei_, _Klebsiella pneumoniae_, _Klebsiella maricola_, _Pseudomonas aeruginosa_, _Pseudomonas fulva_, _Acinetobacter huumanini_, _Brevibacterium casei_, _Corynebacterium fournieri_, _Staphylococcus caprae_, _Acinetobacter lwoffii_, _Serratia liquefaciens_, _Pseudomonas luteola_, _Acinetobacter pittii_, _Acinetobacter radioresistens_, _Pseudomonas juntendi_, _Staphylococcus varneri_, _Staphylococcus saprophyticus_, _Roseomonas gilardii_, _Massilia timonae_, and _Monaxella osloensis_. Reusable tourniquets from the operating theatre showed _Enterobacter quasihormuechei_, _Acinetobacter humumanii_, _Acinetobacter ratioresistens_, _Pseudomonas aeruginosa_, _Pseudomonas juntendi_, _Staphylococcus honinis_, _Staphylococcus taiwanensis_, _Enterococcus faecalis_, _Enterobacter wuhouensis_, _Klebsiella pneumoniae_, _Klebsiella varincola_, _Serratia fonticola_, _Vibrio cholerae_, _Vibrio metschnikovii_, _Brevibacterium casei_, and _Entrococcus hirae_ (Figure 3).  
Samples from the emergency department (SR series) generally showed high bacterial loads of individual species, which could be linked to the rapid, frequent handling of tourniquets in a high-turnover environment. Conversely, samples from the operating theater (SBO series) displayed a broader range of bacteria species, likely due to different handling and environmental exposure patterns, suggesting distinct cleaning or contamination processes in these departments (Figure 3).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Microorganism** & **Percent of HAI Cases in Europe 2022–2023 [cyt]** & **Number of Tourniquets All** & **Number of Tourniquets Emergency Department** & **Number of Tourniquets Operating Theater** \\ \hline _E. coli_ & 12.7\% & 0/12 & 0/6 & 0/6 \\ \hline _Klebsiella_ spp. & 11.7\% & 5/12 & 2/6 & 3/6 \\ \hline _Enterococcus_ spp. & 10\% & 9/12 & 4/6 & 5/6 \\ \hline _Staphylococcus aureus_ & 9\% & 1/12 & 0/6 & 1/6 \\ \hline _Clostridium difficile_ & 8\% & 0/12 & 0/6 & 0/6 \\ \hline _Pseudomonas aeruginosa_ & 7.9\% & 9/12 & 4/6 & 5/6 \\ \hline _Cogulase-negative staphylococci_ & 5.8\% & 11/12 & 6/6 & 5/6 \\ \hline _Proteus_ spp. & 3.2\% & 0/12 & 0/6 & 0/6 \\ \hline _Acinetobacter_ spp. & 3.2\% & 10/12 & 4/6 & 6/6 \\ \hline _Enterobacter_ spp. & 3\% & 0/12 & 0/6 & 0/6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Occurrence of pathogens responsible for Healthcare-Associated Infections (HAIs) on tourniquets.
## 3 Results and Discussion
### Measurement of Mechanical Properties
#### 3.2.2 Compression of Independent Specimens  
Figure 4 presents the findings reflecting mean values of destructive force for samples cut out transversely to the axis of rhapontic rhubarb petioles after ozone treatment was applied at various combinations of doses and duration of exposition, compared to the control. Ozone treatment applied at a rate of 10 ppm for 5 min did not produce increased resistance of the material to damage compared to the control. In the case of the remaining doses and durations of exposure to ozone, the findings showed increased values of destructive force, compared to the control, in all the parts of rhubarb petioles subjected to testing. The highest mean values of destructive force for the top part of rhubarb petioles were identified after application of 100 ppm for 30 min. In the case of the central and bottom part of the petiole, the highest values of this force were found after the treatment at a rate of 100 ppm for 15 min.  
The mean values of destructive force in the process of uniaxial compression of the specimens cut out along the axis of rhapontic rhubarb petioles, after ozone treatment was applied at various combinations of the dose and duration of exposure, compared to the control, are shown in Figure 5. When ozone treatment was applied at a rate of 10 ppm for 5 min, the tests did not show significantly higher values of destructive force compared to the control. When the remaining combinations of dose and duration of exposure to ozone were applied, the highest resistance to damage was found in the bottom segments of rhubarb petioles. Ozone treatment at a rate of 100 ppm for the duration of 15 min resulted in the highest mean values of destructive force in all of the rhubarb petioles parts subjected to testing, compared to the control. [PERSON] et al. [23] investigated the effects of ozone fumigation on the mechanical properties of field cucumbers. The cucumbers were first subjected to ozonation and then to a souring process. The researchers showed that ozone favorably affected the quality of the pickled cucumbers. The cucumbers subjected to ozone treatment prior to the pickling process were characterized by greater resistance of skin and mesocarp to damage, compared to the cucumbers which were processed without the preliminary ozonation. Ozonated parts of rhubarb petioles showed increased mechanical strength, which is associated with potentially less damage during transport, and this may contribute to increasing the profitability of the ozonation process by reducing weight loss due to water loss [24].  
Figure 3: Mean values of the force needed to puncture the skin with \(\varphi=6\) mm indenter, on the flat side of rhapontic rhubarb petioles. Differences in the results between the punching force values for individual variants are marked with different small letters, \(p<0.05\).
# Mechanical Performance Characterization of Lignin-Modified Asphal Mixture
## 3 Materials and Methods
### Asphalt Mixture Design and Preparation  
Stone mastic asphalt (SMA) with a 13.2-mm nominal maximum aggregate size, denoted as SMA-13, was used as the mixture gradation for preparing mixture samples. The gradation curve of SMA-13 is shown in Figure 3 and was designed following the standard Marshall Design method (American Society for Testing and Materials D6927) [8]. The optimum asphalt content for the control SMA-13 was 6.2%. The same asphalt content was used for other asphalt mixtures with lignin to minimize the control variable.  
It is reported that cellulose or polyester fibers were often added into the SMA as the drain-down stabilizer [9]. In this study, lignin was incorporated into asphalt mixture instead to act as a stabilizer. Additionally, the effects of lignin on the mechanical performance of asphalt mixture were also carefully studied. To this end, the lignin powders were first mixed with neat asphalt to prepare lignin-modified asphalt. Based on previous research [2; 7], the content of lignin in the modified asphalt was 10% by weight of asphalt binder, which shows better anti-aging and rheological properties. The required amount of lignin (10% by weight of asphalt binder) was gradually added to the hot asphalt and mixed by a high shear mixing device at 163 \({}^{\circ}\)C for 30 min with a mixing rate of 3000 rpm. Then, the prepared lignin-modified asphalt was used to prepare the asphalt mixture samples. By contrast, the lignin fibers were directly added to aggregates and asphalt to prepare the asphalt mixture samples. Based on the principle of equivalent mass of lignin, approximate 0.62% lignin by the total weight of asphalt mixture  
Figure 1: Visual images (**a**) lignin powder and (**b**) fibers.  
Figure 2: Environmental scanning electron microscope images of (**a**) lignin powder, (**b**) lignin fiber.  
was used. Therefore, in total, three types of asphalt mixture samples were prepared, namely, control mix (SMA-C), lignin powder-modified asphalt mixture (SMA-LP), and asphalt mixture with lignin fiber (SMA-LF).  
The air voids for SMA-C and SMA-LP were around 4.0%. The air void for SMA-LF was 4.5%, slightly higher than 4.0%. No separate mix designs were carried out for the mixtures containing lignin powders/fibers. This means all three types of mixture samples were prepared with the same gradation and asphalt content. The mixing and compaction temperatures were 170 \({}^{\circ}\)C and 155 \({}^{\circ}\)C, respectively. After mixing, the Hot mix asphalt (HMA) mixtures were further aged for 2 h at 160 \(\pm\) 5 \({}^{\circ}\)C in an air-circulated oven before compaction. Specimens with different geometries were fabricated for the following performance tests.
# Hardware Model Checking Algorithms and Techniques
## 1 Introduction
From an algorithmic standpoint, in the beginning, model checking was based on explicit state enumeration; thus, its practical application was severely hindered by mere state space representation problems. The introduction of BDD-based representation [9] was the turning point for model checking applicability to industrial-level scenarios. Despite the huge impact of such an approach to model checking, BDD-based representations still suffered from unpredictable memory requirements and, thus, additional paths of research opened up in order to overcome such limits, such as the introduction of SAT-based approaches. Today, model checking can exploit explicit state enumeration or symbolic state enumeration, via BDD- or SAT-based representations. Furthermore, solutions based on automatic test pattern generations are admissible. Each of these, in turn, has led to a number of distinct model checking algorithms and techniques. Up until today, there is not a concrete winner among all the available techniques. The most reliable approach is obtained from running in parallel several of these algorithms, exploiting multi-core environments, and halting verification as soon as one of the engines has found a solution to the verification problem at hand [10; 11]. Complementary to the verification phase itself, there is then the additional problem of certifying checkers' results, in order to better support their acquisition in complete design cycles scenarios [12; 13]. A recent trend in hardware model checking is to exploit the ability of SMT solvers to perform word-level reasoning based on bit-vectors. This trend is demonstrated by the introduction of word-level tracks in recent editions of the Hardware Model Checking Competition [14]. However, word-level model checking shines for problems with memory modeled with arrays; bit-level solvers are currently the de facto standard in the industry and prove to be still superior on many word-level designs after bit-blasting [15]. This paper (part of the contents of this paper are a redacted version of the introductory chapters of the PhD thesis of two of its authors, [PERSON] [16] and [PERSON] [17], which have not been published before besides diffusion within the Politecnico di Torino and the respective thesis defense committees) describes the most relevant algorithms and techniques targeting explicitly bit-level SAT-based hardware model checking, in a formal and theoretically sound fashion. A description of complementary topics and a broader overview of the vast field of model checking and satisfiability is provided in [18; 19]. A description of topics concerning model checking, with additional examples and applications, is provided in [8]. Complementary to previous works, a specific focus on the design and verification of cyber-physical systems is provided in [20].