esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The realization of robots that can play physical games with humans is a challenging research topic in the field of human-robot interaction research. Many robots that play physical games with humans have been developed to date, for games such as table tennis [1], playing catch [2, 3], handball [4], volleyball [5], and air hockey [6, 7]. Such a robot can be applied not only for entertainment purposes [2, 7, 8, 9] but also in special needs education, such as helping in the development of children with autism [10, 11]. For example, we developed a robot that plays \"Daruma-san ga Koronda\" [12, 13], a children's game similar to \"Reed light, Green light.\" To extend this aspect of a robot that can play physical games with children, we considered a more active game such as play tag.  
One of the difficulties in playing such games is detecting and tracking players who actively move in the field. [PERSON] et al. developed a robot system to play tag [14]; however, in their system, the robot's role was fixed on the fleeing player, so the robot did not need to track the player. In our system, the robot is intended to be both \"it\" and the other player. Therefore, the robot needs to detect and track other players, chase other players, and touch them when the robot is being \"it\", and run away from \"it\" by avoiding collisions with other players or objects when the robot is not being \"it\".  
This paper focuses on developing a robot that plays the role of \"it\", which chases other players and touches the nearest player. Usually, the goal of mobile robots that move in a crowd is to arrive at the specified location without colliding with other persons and obstacles [15]. On the other hand, the goal of a robot that plays tag with the role of \"it\" is not to avoid other players but to approach one of the players and touch them without colliding with the players. To achieve this task, the robot needs to track multiple players around it, focus on one player, approach the player, determine the target point at which the robot touches the target player, and move away from the player. We assumed a two-wheeled robot as a platform for this work. We could use a biped robot or an omnidirectional mobile robot--however, compared with these types of robots, a two-wheeled robot is easier to control than a biped robot, and mentally safer than an omnidirectional mobile robot because a player can predict the direction in which the robot is about to move.  
We already evaluated a method for tracking a single player that moved quickly [16]. As a next step, we developed a method to approach a player and pass by the player within a distance of the robot's arm length. This paper proposes a robot control method that realizes the robot's \"touch-and-away\" behavior.
## 3 Results
### Multi-Physical Data Approaches  
In Figure 5, the \(x\)-axis displays the various modalities found in articles classified as multi-physical because they use more than one type of physical data in their analysis. On the \(y\)-axis, the quantity of articles for each modality and their distribution across the years are shown. Among the most prominent are studies that use the fusion of audio-text-visual, audiovisual, and audio-text modalities. Additionally, there is a notable trend with a focus on facial analysis fused with micro-expressions, which are subtle and fleeting emotional signals. This demonstrates a growing interest in understanding and detecting emotions in situations where facial expressions are brief and difficult to capture. From additional analysis of articles classified as multi-physical, several important points can be highlighted:  
* Most studies employ CNNs and RNNs, while others utilize variations of general neural networks, such as spiking neural networks (SNN) and tree-based neural networks. SNNs represent and transmit information through discrete bursts of neuronal activity, known as \"spikes\" or \"pulses\", unlike conventional neural networks, which process information in continuous values. Additionally, several studies leverage advanced analysis models such as the stacked ensemble model and multimodal fusion models, which focus on integrating diverse sources of information to enhance decision-making. Transfer learning models and hybrid attention networks aim to capitalize on knowledge from related tasks or domains to improve performance in a target task. Attention-based neural networks prioritize capturing relevant information and patterns within the data. Semi-supervised and contrastive learning models offer alternative learning paradigms by incorporating both labeled and unlabeled data. * The studies address diverse applications, including sarcasm, sentiment, and emotion recognition in conversations, financial distress prediction, performance evaluation in job interviews, emotion-based location recommendation systems, user experience (UX) analysis, emotion detection in video games, and in educational settings. This suggests that emotion recognition thorough multi-physical data analysis has a wide spectrum of applications in everyday life. * Various audio and video signal processing techniques are employed, including pitch analysis, facial feature detection, cross-attention, and representational learning. Moreover, Table 6 lists the databases identified from the studies classified as multi-physical, offering a brief description of each, including their respective advantages and limitations. Some of the references next to the database name indicate one of the studies in which this database was used, or the actual study in which the database was made public.
## 4 Compressive Behavior
### Effects of Impact Velocity and Mass  
Three crushing velocities, 3, 5, 20 and 50 m/s, along the Y and -Y directions are, respectively, applied to the bottom and top rigid plates of the 3D AH model so as to investigate the effects of impact velocities on the behavior of PR. The same aforementioned FE model of 3D AH is adopted in this study, and the ultimate crushing strain of the 3D AH model is 70%. Figure 9 shows the variation in normalized stress \(\overline{\sigma}\) and plastic energy dissipation \(\overline{U}_{P}\) with crushing strain. Here, the normalized stress is defined as \(\overline{\sigma}=\sigma/\overline{p}\), and the normalized plastic energy dissipation is expressed as \(\overline{U}_{P}\)= U\({}_{\rm P}\)/\(\sigma_{\rm YC}\)AL\({}_{0}\), where U\({}_{\rm P}\) is the plastic energy dissipation and \(\sigma_{\rm YC}\)= 0.5\(\overline{p}^{2}\sigma_{\rm Y}\) is the effective yield stress for chiral structures.  
Figure 8: (**a**) Deformed shapes of 3D AH against the crushing strain up to 50% at impact speed of 3 m/s (**top left**), 5m/s (**middle left**), 10m/s (**bottom left**), 20m/s (**top right**) and 50 m/s (**middle right**), respectively. (**b**) PR of 3D AH with strain corresponding to the impact speed between 3 m/s and 50 m/s.  
It can be seen from Figure 9a that the normalized stress is inclined to exhibit a plateau phase after experiencing peak stress under high-velocity impact. However, when subjected to relatively low impact velocities, like 3 m/s and 5 m/s, no obvious peak stress can be observed. The curves plotted in Figure 9b showed a growing trend of plastic energy dissipation when increasing the impact velocities. This results from the more effective plastic deformation of materials in the vicinity of impact.  
The effect of impact mass on the dynamic response of 3D AH is intuitively depicted in Figure 10, which plots the crushing force-deformation curves corresponding to variable impact mass (m = 12 kg, m = 20 kg, m = 28 kg and m = 35 kg) but constant initial impact velocity of \(v_{0}\) = 5 m/s. It is observed that the change in initial impact mass brings about nothing but the maximum crushing deformation of the structure. The implication is that the crushing process relies on the initial kinetic energy possessed by the impactor, which determines the ultimate structural deformation but not the dynamic characteristic of impact force if constant initial impact velocity is adopted.  
Figure 10: Crushing force–deformation curves of 3D AH under different impact mass increasing from m = 12 kg to m = 35 kg.  
Figure 9: Compressive response of chiral structures under various impact velocities. (**a**) Normalized stress against strain; (**b**) normalized plastic energy dissipation against strain.
## 3 Results
### Effects of CJ on Hepatic and Renal NADPH Oxidase Expression  
We aimed to measure the NADPH oxidase (NOX) protein, which plays not only a pathophysiological role in diabetic pancreatic \(\beta\)-cells but also a physiological role, responding to various factors as a membrane-associated multi-subunit enzyme [27] in the liver and kidney. The results revealed a significant increase in expression in the Veh group compared with the Normal group (Figure 4), whereas a dose-dependent decrease in expression was observed in the CJ administration group. Additionally, a substantial increase in p47 phox expression was observed in the Veh group, while a significant decrease was observed in the CJ 100 group.  
Figure 4: Hepatic NADPH oxidase subunit related protein expression. (**A**) NOX-4 expression; (**B**) p47 phox expression. Normal, non-diabetic rats; Veh, STZ 30 mg/kg-treated rats; CJ 50, STZ 30 mg/kg + CJ 50 mg/kg-treated rats; CJ 100, STZ 30 mg/kg + CJ 100 mg/kg-treated rats; AG 100, STZ 30 mg/kg + aminoguanidine 100 mg/kg-treated rats. Values are expressed as the mean \(\pm\) SEM (\(n\) = 8). **\(p\)** \(<\) 0.01, **\(p\)** \(<\) 0.001 compared with Normal. * \(p\)** \(<\) 0.05, and **\(p\)** \(<\) 0.001 compared with Veh.  
Figure 3: Effects of CJ on renal (**A**) glucose concentration, (**B**) triglyceride production, (**C**) ROS production, and (**D**) LDH production in STZ-induced rats. Normal, non-diabetic rats; Veh, STZ 30 mg/kg-treated rats; CJ 50, STZ 30 mg/kg + CJ 50 mg/kg-treated rats; CJ 100, STZ 30 mg/kg + aminoguanidine 100 mg/kg-treated rats. Values are expressed as the mean \(\pm\) SEM (\(n\) = 8). **\(p\)** \(<\) 0.01 compared with Normal. * \(p\)** \(<\) 0.05 and **\(p\)** \(<\) 0.01 compared with Veh.  
The expression of both of these proteins (NOX-4 and p47 phox) was significantly increased in the Veh group compared with the Normal group. However, the NOX-4 and p47 phox expressions in the CJ and AG groups were significantly decreased compared with the Normal group. In particular, p47 phox expression displayed a dose-dependent decrease in the CJ groups (Figure 5B). These findings suggest a potential inhibitory effect of CJ on hepatic and renal NADPH oxidase activity, as indicated by the reduced expression of key proteins associated with this pathway.
## 3 Results and Discussion
### Regeneration of Spent Sorbent  
According to the literature, P sorption on bimetal oxide occurs via the replacement of surface hydroxyl groups (M-OH) by P to form inner-sphere complexes [31]. The metal oxide was effectively regenerated by NaOH solution. The P adsorption process can be described as:  
\[3\equiv\text{M}-\text{OH}+\text{PO}_{4}{}^{3-}\rightleftharpoons(\equiv\text{M}) _{3}\text{PO}_{4}{}^{3-}+3\text{OH}^{-}\text{ (M = Al and Ti)} \tag{22}\]  
As P adsorption onto Al-Ti oxide proceeds over time, the adsorption active sites of the sorbent gradually became saturated with P, causing a reduction in adsorption capacity. It can be anticipated that improved adsorption occurs through sorbent regeneration. The results of consecutive adsorption/regeneration cycles are illustrated in Figure 10. Compared with the adsorption capacity of 68.9 mg P/g and removal rate of 93.6% in the first use of Al-Ti oxide, the adsorption capacity and P removal rate reached 57.6 mg/g and 76.9%, respectively, after five consecutive adsorption/regeneration cycles. Therefore, it can be concluded that Al-Ti oxide exhibited good reusability for P removal.
## 2 Brief Literature Review and Research Gaps  
Scheduling problems are considered to be of very complex resolution. Not only are they NP-complete [45], but they are also actually among the most difficult problems to solve because of the difficulty of computing solutions [46]. The joint scheduling problem addressed in this paper is even more complex as it involves not only assigning production jobs and maintenance activities to machines but also accounting for the impact of learning and deteriorating effects on processing times.
## 2 Materials and Methods
### Enzyme-Linked Immunosorbent Assay (ELISA)  
The CD63 protein levels in biofluids including urine, plasma, serum, and saliva were measured using two commercially available ELISA kits.  
For urine and saliva samples, a human CD63 ELISA kit (Cusabio, Wuhan, China) was used following the manufacturer's instructions. Briefly, diluted standards (0.625-40 ng/mL) and samples (100 \(\upmu\)L/well) were incubated at 37 \({}^{\circ}\)C for 2 h. Without washing, biotin-conjugated detection antibody was added and the mixture was incubated at 37 \({}^{\circ}\)C for 1 h. After three washes, HRP-avidin was added and the mixture was incubated at 37 \({}^{\circ}\)C for another hour. Following five washes, a TMB substrate was added and the mixture was incubated in the dark for 15-30 min at 37 \({}^{\circ}\)C. Stop solution (50 \(\upmu\)L) was added, and the absorbance was measured at 450 nm with wavelength correction at 540 nm. The concentrations were calculated using a four-parameter logistic (4-PL) curve-fitting model.  
For plasma and serum samples, the Human AN30 (Tetraspanin 30) ELISA Kit (ELK Biotechnology, Denver, CO, USA) was used. The experimental procedure was performed according to the manufacturer's instructions, including sample and standard preparation, incubation, washing, and detection steps. Absorbance was measured at 450 nm, and CD63 concentrations were calculated based on the provided standard curve.
# A Thermal Discomfort Index for Demand Response Control in Residential Water Heaters
## 5 Experimental Validation
### Methodology
#### 5.1.1 Validation of Forecasting Models  
The first step of the experimental evaluation consists of validating the two models required to calculate the TDI (water utilization forecasting and water temperature). For the validation of the aforementioned models, the standard mean absolute percentage error (_MAPE_) metric is used to evaluate the forecasting capabilities. _MAPE_ is defined in Equation (8), where \(actual_{i}\) represents the measured value for \(t=i\), \(pred_{i}\) represents the predicted value, and \(n\) is the predicted horizon length.  
\[MAPE=100\times\frac{1}{n}\sum_{i=1}^{n}\Big{|}\frac{actual_{i}-pred_{i}}{actual _{i}}\Big{|} \tag{8}\]
## 4 Discussion  
### Impact of Killing Method on Microbial Load  
Heating at the appropriate temperature (100 \({}^{\circ}\)C) and for the appropriate duration (40 s) has a major influence on the reduction of microbial loads in BSFs after killing [8], which is able to reduce the total aerobic viable count by around 10 times compared with other methods. As we know, the growth rate of bacteria is reduced in a cold temperature environment, which can result in a low microbial load in BSFs after killing by freezing methods [8]. The total bacterial count in the blending method was similar to that of the asphyxiation method in this trial; however, the study of [PERSON] et al. [8] reported a lower value of this parameter in the blending group than with the asphyxiation method. The blending method provided a homogenized sample which destroyed the cell structure and increased the contact surface between bacteria and substrates which could promote bacterial growth. Black soldier fly prepupae can survive under asphyxiation for long periods, in contrast to house crickets (Acheta domestica [23]). Therefore, this technique promoted anaerobic conditions and prolonged the killing process at room temperature, which led to selective growth of a specific group of microbes. For these reasons, higher microbial loads of facultative anaerobic bacteria and anaerobic bacteria were observed in the asphyxiation treatment groups [8]. The rearing method and biosecurity system are important factors which correlate with the microbial load and community in BSFs [24]. The absence of contamination by _Acinetobactor_ sp., _Bacillus cereus_ and _Salmonella_ spp. during cultivation and processing contributed to the absence of growth of these bacteria in BSFs. The absence of _Salmonella_ spp. in raw materials is an important quality and safety indicator according to the regulations in several countries; therefore, good agricultural and biosafety practices must be strictly performed [25] to prevent contamination along the production process. However, contamination with coliforms and other pathogenic bacteria was discovered in this study, which came from the BSF substrates. Heat treatment was suggested to reduce these microorganisms. The overall number of bacteria in this study seemed higher than in the study of [PERSON] et al. [8]. The different outcome may have been a consequence of the bacterial culture being immediately performed after the end of the killing procedures in this study, whereas [PERSON] et al. [8] cultured
## 6 Use Case: Privacy Identity Management Enrollment
### Registration  
During this phase, the user creates a new account with the virtual Identity Provider (vIdP). Enrollment involves providing a username, password, and optionally a set of user attributes. As a second authentication factor, an authenticator or code-generation app is used, which generates a One-Time Password (OTP) based on a user-specific seed and timestamp. This registration process is interactive and takes place between the user and all the identity providers that collectively form the vIdP. The registration process is outlined in the following steps:  
1. Administrator Login: The system administrator logs into the AURORA MINDS platform using their credentials (username and password).
2. User Role Selection: Once authenticated, the administrator chooses the type of user to be registered (i.e., clinician, parent, or children).
3. Clinician Registration: The administrator fills in the clinician's details, sets a username, and generates an OTP.  
Instead of relying on a single shared seed, each partial Identity Provider (IdP) in the virtual IdP (vIdP) system maintains its unique seed. Consequently, the user's One-Time Password (OTP) is generated as a combination of individual OTPs derived from each partial IdP's seed. To verify the authenticity of the OTP, the partial IdPs can exchange their respective portions of the OTP corresponding to the same timestamp, allowing them to collaboratively validate the result. Additionally, user attributes--such as roles or certifications--are obtained from a trusted external Attribute Provider. The vIdP verifies the validity of these attributes before storing or associating them with the user account.  
At the end of the registration protocol, the user receives a confirmation message indicating the successful completion of the account creation. From this moment, the user has an account in the Verification System vIdP.
## 3 VRDeepSafety Simulation Environment
### V2X Communication Integration
#### 3.7.3 Communication Performance  
The performance of V2X communication is significantly influenced by both range and signal reliability. IEEE 802.11p, while effective at short distances, experiences performance degradation in crowded environments due to interference. In contrast, Cellular-V2X (C-V2X) offers a longer range and higher reliability but is susceptible to increased latency in high-traffic areas or during network congestion.  
Despite the advantages of these protocols, real-world communication faces obstacles like signal attenuation, network congestion, and temporary data loss. These can impact the efficiency and accuracy of V2X. To address these issues, VRDeepSafety has a robust mechanism to continue to operate under less-than-ideal conditions. By training the VRFormer model to be accurate even with incomplete or delayed V2X data, VRDeepSafety ensures it works in all real-world scenarios.  
This approach enables VRDeepSafety to effectively handle communication link disruptions--whether caused by environmental conditions, network congestion, or momentary signal loss--without compromising autonomous vehicle safety or responsiveness. The system's inherent adaptability enables consistent, real-time decision-making, which is essential for maintaining safety and optimizing traffic flow.  
To quantify the impact of signal degradation, we use the Free-space Path Loss (FSPL) equation, which models the attenuation and interference experienced in real-world environments:  
\[\text{FSPL}=20\cdot\log_{10}(d)+20\cdot\log_{10}(f)+20\cdot\log_{10}\left(\frac{4 \pi}{c}\right) \tag{20}\]  
where \(d\) is the distance between the transmitter and receiver in meters, \(f\) is the frequency of the communication signal in Hertz, and \(c\) is the speed of light, approximately \(3\times 10^{8}\) meters per second. This FSPL equation provides a way of modeling how signal strength decreases with distance, which allows us to evaluate communication performance under different conditions within the VRDeepSafety environment.
## 3 Region-Based Collision Avoidance Beaconless Geographic Routing
### Distance- and Angle-Based Collision Avoiding Scheme
\(\delta\) and \(D\) are represented as follows:  
\[\delta=\tan^{-1}\frac{y_{d}-y_{s}}{x_{d}-x_{s}},\qquad D=\sqrt{(x_{d}-x_{s})^{ 2}+(y_{d}-y_{s})^{2}} \tag{8}\]  
Figure 5: Sector area division for data forwarding. A small number of sectors means a short timer. \(e\), \(g\) and \(h\) are Sectors 1, 5 and 7, respectively, whereas \(c\) and \(f\) are Sector 3. Furthermore, every sensor node has another timer called \(T_{interval}\) for the adoptive collision avoidance. After a sender node broadcasts the data packet, if the node realizes a collision among its neighbors by the MAC device or receives two or more packets from its neighbors within \(T_{interval}\), it determines that there might be a collision among neighbors and rebroadcasts data with increased \(T_{max}\).