esa-sceva/satcom-chunk-collection huggingface dataset:
# Thermal Hydrolysis of Sewage Sludge: A Case Study of a WWTP in Burgos, Spain
## 2 Materials and Methods
### Mass and Energy Balance of the Thermal Hydrolysis Unit  
The use of live steam is needed in THP for increasing the temperature of sludge to the desired conditions. Live steam is generated in a recovery mixed boiler that obtains heat from engine exhaust gases; biogas used as fuel whenever necessary. The boiler feeding water is preheated using the energy from the main engine's cooling circuit. Estimation of live steam demand was performed for the average batch cycle. The Cambi process operates using a batch configuration, making it difficult to compare with continuous processes. The mass balance for the batch cycle leads to similar results when an average cycle is assumed; this significantly simplifies calculations and allows for easier comparison between different technologies.  
The total mass of treated sludge from the THP-B6.2 reactor was 6300 kg/batch with a concentration of 12.7% TS (data obtained from the WWTP of Burgos). The reactor is the only element operating under batch mode, whereas the pulper and flash-tank operate under continuous mode. Process gases produced in the hydrolysis reactor were considered in the mass balance of the pulper unit. These gases are sent to digesters for further treatment. The flash steam from the reactor (30 kg/batch) was assumed to have been sent to the pulper instantaneously. This stream contains organic material derived from the release of process gases of up to 8 kg VS/batch. Figure 2 presents the process scheme and nomenclature of each of the variables involved in the calculation stage of the mass and energy balance of a CambiTM hydrolysis thermal unit for the average cycle. Only one reactor is represented in this figure. The mass and energy balances are described in Appendix A.
# Exploring Decentralized Warehouse Management Using Large Language Models: A Proof of Concept
## 2 Theoretical Background
### Multi-Agent Systems (MASs)  
MASs involve multiple autonomous agents interacting within a shared environment to achieve individual or collective goals [18]. Agents in MASs are characterized by autonomy, social skills, reactivity, and proactivity [19]. The mathematical model of an MAS comprises equal agents (Equation (1)):  
\[A=\{a_{1},a_{2},\ldots,a_{n}\} \tag{1}\]  
For each agent \(a_{i}\), the state space \(S_{i}\) is defined, which is a set of possible states of the agent. The action space \(A_{i}\) is a set of actions available to the agent. The perception function \(\text{Percept}_{i}:E\to O_{i}\) maps the environment \(E\) to the agent's observations \(O_{i}\). The decision function (policy) \(\pi_{i}:H_{i}\to A_{i}\), where \(H_{i}\) is the history or set of observations and states for the agent, determines the action selection. The state transition function \(T_{i}:S_{i}\times A_{i}\to S_{i}\) defines how the agent's state updates based on its actions. Finally, the reward function \(R_{i}:S_{i}\times A_{i}\to\mathbb{R}\) represents the agent's objective in terms of rewards.  
The agents interact within a shared environment \(E\) characterized by its own state space \(S_{E}\) and state transition function \(T_{E}\). The environment's state is updated based on the collective actions of all agents (Equation (2)):  
\[T_{E}:S_{E}\times A_{1}\times A_{2}\times\cdots\times A_{n}\to S_{E}. \tag{2}\]  
After each discrete time step \(t\), the next one occurs. Each agent \(a_{i}\) observes the environment using its perception function (Equation (3)):  
\[a_{i}(t)=\text{Percept}_{i}(E(t)). \tag{3}\]  
Based on its policy \(\pi_{i}\) and history \(H_{i}(t)\), the agent selects an action (Equation (4)):  
\[a_{i}(t)=\pi_{i}(H_{i}(t)). \tag{4}\]  
The agents execute their actions, which affects the environment and possibly other agents. The agents then update their internal states according to their state transition functions (Equation (5)):  
\[s_{i}(t+1)=T_{i}(s_{i}(t),a_{i}(t)). \tag{5}\]  
Simultaneously, the state of the environment is updated based on the state transition function (Equation (6)):  
\[E(t+1)=T_{E}(E(t),a_{1}(t),a_{2}(t),\ldots,a_{n}(t)). \tag{6}\]This mathematical framework is utilized in our system design to model the interactions and decision-making processes of agents within the decentralized warehouse management system.
# A Hybrid Control Approach for the Swing Free Transportation of a Double Pendulum with a Quadrotor
## 2 Dynamic Modeling of the System
### Quadrotor Dynamics  
The governing equations of the lift and torques generated by the propellers are as follows:  
\[\begin{array}{rcl}U_{1}&=&b\left(\Omega_{1}^{2}+\Omega_{2}^{2}+\Omega_{3}^{2}+ \Omega_{4}^{2}\right),\\ U_{2}&=&l\cdot b\left(-\Omega_{2}^{2}+\Omega_{4}^{2}\right),\\ U_{3}&=&l\cdot b\left(-\Omega_{1}^{2}-\Omega_{3}^{2}\right),\\ U_{4}&=&d\left(-\Omega_{1}^{2}+\Omega_{2}^{2}-\Omega_{3}^{2}+\Omega_{4}^{2} \right),\end{array} \tag{3}\]  
where \(U_{2}\), \(U_{3}\) and \(U_{4}\) are the roll, pitch and yaw torques, respectively, and \(\Omega_{i}^{2}\) is the squared speed of the \(i\)-th propeller, and \(U_{1}\) is the total thrust of the quadrotor. The parameter \(b\) and \(d\) are the thrust and drag coefficients, respectively.  
Angles \(\phi\), \(\theta\), and \(\psi\) represent the Euler angles referring the Earth inertial frame with respect to the body-fixed frame of quadrotor (\(\phi\), \(\theta\) and \(\psi\) represent the rotation along axis \(X\), \(Y\) and \(Z\), respectively). Equation (4) provides necessary modifications for angular and linear accelerations (considering that \(\psi\), \(\dot{\psi}=0\)). \(I_{XX}\), \(I_{YY}\), \(I_{ZZ}\) are the inertia moments around the \(X\), \(Y\) and \(Z\) axis, and \(m\) is the mass of the quadrotor. Finally, \(T_{1Z}\), and \(T_{1X}\) are the \(T_{1}\) components in \(X\) and \(Z\) directions.  
\[\begin{array}{rcl}\ddot{x}&=&\frac{\cos\phi\sin\theta}{m}U_{1}+T_{1X}\\ \ddot{y}&=&\frac{-\sin\phi}{m}U_{1}\\ \ddot{z}&=&(\cos\theta\cos\phi)\frac{U_{1}}{m}+g+T_{1Z}\\ \ddot{\phi}&=&\frac{U_{2}}{I_{xx}}\\ \ddot{\theta}&=&\frac{U_{3}}{I_{yy}}\\ \ddot{\psi}&=&\frac{U_{4}}{I_{zz}}\end{array} \tag{4}\]  
The integration of Equation (4) is calculated with the Euler method. Though the numerical integration procedure can be critical for highly nonlinear systems, such as chaotic systems [42], our system is smooth enough to allow for this computationally cheaper approach. Considering that the control horizon of MPC control for the quadrotor application is \(1s\), we have found in the recent literature that Euler provides a good balance of numerical precision and stability, and quick response for real time applications [43; 44].
# A Survey of Vehicle to Everything (V2X) Testing
## 2 V2X Applications  
The V2X represents a new cross-industry event involving automobiles, transportation, communications, and the Internet. Various industries and cross-industry alliances have researched the requirements for business applications of V2X. Currently, applications that have been developed or that  
Figure 1: LTE-V2X Communication Modes. (**a**) Uu mode for LTE-V2X (**b**) PC5 mode for LTE-V2X.  
will be applied in the short term can be classified into three categories: safety applications, efficiency applications, and information services applications [27]. Safety applications refer to applications involving personal safety, such as collision warnings, road hazard warnings, and speeding warnings. Efficiency applications refer to applications that guide owners to drive and improve traffic efficiency, such as green wave speed guidance and congestion warnings. The information services applications refer to applications that provide owners with vehicle-related information to improve the driving experience, such as eCall, traffic information and route recommendations, and automatic parking. With the development of communication technology, the V2X will gradually meet the requirements for advanced automatic driving and applications in intelligent traffic systems. 3 GPP defines four types of applications for these advanced application scenarios: Vehicle Platooning, Advanced Driving, Extended Sensors, and Remote Driving [22]. The above applications are all new applications resulting from the development of V2X. However, many traditional mobile applications will gradually enter the V2X industry, such as entertainment services.  
3 GPP has developed corresponding requirements for different V2X applications, among which latency/ reliability requirements and safety requirements are the top priorities [23]. The above applications are classified according to latency/reliability and security in Figure 2. The abscissa indicates the application's requirement for latency and reliability. The larger the abscissa is, the lower the latency required for the application, and the higher the reliability. The ordinate indicates the application's requirement for security, and the greater the ordinate is, the higher the security level of application security.
## 4 Discussion  
The values of L *, a *, b *, and C * tended to increase during the blooming process. A similar trend was observed in LL and SM muscles in several previous studies [15; 16; 18; 24; 31; 32]. An analysis of \(\Delta\)E\({}_{0}\) revealed that the most pronounced changes in color occurred during the first 10 min of blooming; relative to the color parameters determined after 60 min of blooming, the changes noted in the LL muscle accounted for 69%, 53%, and 51%, whereas the changes noted in the SM muscle accounted for 95%, 69%, and 61% on days 1, 9, and 14 of ageing, respectively. It was also found that the optimal blooming time (color stabilization time) increased proportionally to ageing time (5, 20, and 25 min for the LL muscle and 10, 15, and 20 min for the SM muscle, on days 1, 9, and 14 of ageing, respectively). Moreover, color stabilized faster in the SM muscle than in the LL muscle. The researchers who studied the blooming process highlighted the relevance of the first minutes after cutting meat. However, they analyzed individual color parameters instead of using \(\Delta\)E, which combines all color coordinates. [PERSON] et al. [22] reported an over 50% increase in the values of L *, a *, and b * in _longissimus thoracis_ (LT) steaks, which occurred within the first 10 min after cutting, and an over 90% increase in the values of color parameters that occurred during 60 min of blooming. [PERSON] et al. [16] found no changes in L * values during 93 min of the blooming process, and reported an increase in a * values during the first 9 min followed by their stabilization after 12 min, and an increase in b * values during the first 6 min and their stabilization after 6 min. The above results suggest that blooming times in excess of 12 min are necessary to obtain the most stable and repeatable color measurements on freshly cut beef [16]. More recently, these findings were confirmed by [PERSON] et al. [33] who studied blooming in lamb meat and reported that the process did not affect the lightness of meat aged for 6 and 12 days, but increased other color parameters (a * and b *). They suggested that the appropriate blooming time for 12-d aged lamb meat was 18 min. The muscles analyzed in this study differ in terms of their functions in live animals--LL is a positional muscle important for maintaining the posture, whereas SM is a primary locomotive muscle [27]. The main functions of these muscles affect their composition (muscle fibers, connective, adipose, vascular, and nervous tissues) and color [34; 35]. More active muscles, such as SM, are darker than the remaining muscles, including LTL [34], which was also noted in this study. As expected, muscle type had a significant (\(p<\) 0.001) influence on lightness (L *), redness (a *), yellowness (b *), and chroma
## 3 Results
### Evaluation of Gabor Filter Template  
The produced Gabor templates \(G\), whose x- and y-axes correspond to the bearing and time, are shown in Figure 4. The template library settings are based on the typical movement of actual targets at sea; therefore, it is assumed that the targets' maximum speed \(v_{max}\) is 40 kn (about 74 kn per hour), the system response time \(T_{min}\) is 13 s, and the system's minimum effective distance \(d_{min}\) is 2 kn. The gray values correspond to the strength of the synthetic signals at \(<\theta,t>\), and \(G_{i}\) is the \(i\)-th template in the template library. The bearing range of \(G_{i}\) is roughly determined as \(\beta=(90\times v_{max}\times T_{min})/(\pi\times d_{min})\approx 3.83^{\circ}\). To ensure easy calculation, \(\beta\) is set to \(4^{\circ}\) in this experiment. According to the parameters mentioned above, we set the start bearing \(\theta=(i-9)\times\Delta\theta_{step}\) and the end bearing \(\theta^{{}^{\prime}}=(9-i)\times\Delta\theta_{step}\) for any \(G_{i}\) (\(i=1,2,3,\cdots,17\)). This subsection then assesses the paper's Gabor template generating approach. The bearing resolution \(\Delta\theta_{step}\) is 0.5 degrees, and the height of a single template is 13-snapshot intervals comparable to \(T_{min}=13\) s, and the spatial aspect ratio \(\gamma\) of the stripes is 0.05, indicating that the Gabor function should extend along the time axis. As illustrated in Equation (35), the Gabor function is a Gaussian function modulated by a sinusoidal function (\(\cos\left(\frac{2\pi\theta}{\lambda}+\phi\right)\)), and the initial phase is zero; therefore, the first null point \(\theta_{firstNull}\) is determined by \(\frac{2\pi\theta_{firstNull}}{\lambda}\). Let \(pi/2=\frac{2\pi\theta_{firstNull}}{\lambda}\), then \(\theta_{firstnull}=lambda/4\); therefore, if we want the template width to be 3, the stripe width \(\lambda\) will be \(3\times 4=12\). The energy optimum of a Gaussian function is in the \([-3\sigma,3\sigma]\) range; thus, if \(3\sigma=\lambda/4\), we obtain \(\sigma=\lambda/12\), and the standard deviation \(\delta\) of the Gaussian component is \(\lambda/12=12/12=1\), as indicated in Equation (35).  
Under the assumption of a linear model, the two-dimensional time-bearing template-library set above covers all target-bearing modes; that is, when the prior TMA parameters are specified, Gabor filtering can generate the corresponding matching BTR trajectory within the set time.
# Power Grid Reliability Evaluation Considering Wind Farm Cyber Security and Ramping Events
## 4 Attack Scenarios  
To quantify the consequences of cyber-attacks, two attack strategies scenarios were analyzed. The first scenario was the worst-case attack which trips the WT to maximize the load shedding on the system's vulnerable moment. The second scenario was the imperfect attack, which aims to accelerate the aging and fatigue of a WT. These two attack scenarios affect power system reliability in different time scales. The worst-case attack directly leads to a serious power imbalance, which is a short-term impact to the power system. The IPA increases the failure rate of a WT and deteriorates system reliability in a long term.
# The State-of-the-Art of Sensors and Environmental Monitoring Technologies in Buildings
## 2 Sensors and Monitoring Technologies
### Light Sensors  
Light sensors are passive devices designed to convert light energy (typically ranging in the light spectrum from 'Infrared' (3 GHz-400 THz) to 'Visible' (430-770 THz) and, in some cases, up to 'Ultraviolet' (800 THz-300 THz) into an electrical output signal which can be used to switch/dim lights ON and OFF when the light level is above or below a certain limit [65].  
The two most common types of daylight sensors in use today are (1) photoresistors; electronic devices whose resistance decreases with increasing light intensity and vice versa and (2) photodiodes; electronic devices that can convert light into a current or voltage signal. Both photoresistors and photodiodes generally have similar accuracies (\(\pm\)5%-10%), wired and wireless capabilities, and suitability for both indoor and outdoor environments. However, photodiodes have faster response times due to having both analog and digital capabilities, lower energy consumption, and tend to be cheaper in cost, which makes these devices more widely used in comparison to photoresistors for building applications (see Table 2) [66; 67; 68; 50; 65].
## 5 The Concept of IoV
### Security Threats and Attacks in IoV Environments
#### 5.6.4 Denial-of-Service Attack  
Every network has a bandwidth within which it operates. Hence, a denial-of-service (DoS) attack feeds a large amount of information to a particular communication channel legally. In this way, the attacker congests the channel and slows it down. While the network is down, the attacker can use the limited resources for illegal business [60]. When communication among legitimate vehicles is jammed, no form of communication can take place. This leads to confusion and panic because these vehicles depend on this communication to make informed decisions such as what routes to avoid [106]. Moreover, information relayed on the network can save lives by preventing accidents from happening. DoS attacks can be categorized into three types [107]:  
* Malicious--there is an objective behind the attack;
* Disruptive--the attack has the potential to degrade the network;
* Remote--the attack does not emanate from within the network.
# A Comparative Analysis of Anomaly Detection Methods in IoT Networks: An Experimental Study
## 4 Results
### Complete Results Summary  
In the case of supervised ML, the Random Forest algorithm consistently achieved the highest results in all the studies analyzed for both the IoT-23 and CIC-IoT 2023 datasets. The accuracy of the RF model ranged from 99.16% to 100%, indicating its high performance in anomaly detection. For unsupervised ML, Autoencoder also showed high performance, achieving an accuracy of 98.76% to 99.2697%. This suggests that Autoencoder is a promising tool for anomaly detection in IoT networks. Isolation Forest's results were more variable, with the effectiveness of the method depending on the dataset and model configuration. In some cases, Isolation Forest performed satisfactorily, e.g., it produced an F1-score of 100% for the IoT-23 dataset. However, in other cases, its performance was lower, e.g., low precision, sensitivity, and F1-score values were produced for the CIC-IoT 2023 dataset. This suggests that Isolation Forest can be an effective tool in some situations but requires careful configuration and may not be suitable for all data types.
# Model Updating of Bridges Using Measured Influence Lines
## 3 Results
### Updated FE Model
#### 3.2.3 Frequency & MAC-Based EDMF  
The MAC values of some modes never exceeded the 0.90 value. There may have been several reasons for this [36], the sparse and/or improperly distributed sensor layout concerning the geometric complexity of the tested structure [37] being among the most likely. Despite that, it was expected that MAC criteria would improve results once added to the frequency-based EDMF. Particularly because the low MAC values also indicated incorrectly paired modelled and measured modes. As such, it would complement the frequency-based EDMF with more reliable candidates. Figure 21 shows the frequency and MAC-based EDMF results. The threshold for frequencies was set to [0.95, 1.05]. The upper thresholds for MAC were set to the absolute value of 1.00, and the lower thresholds to 0.78.  
Figure 21: Frequency and MAC-based EDMF results. Experimental values are denoted by red markers, red horizontal lines indicate thresholds, candidates are represented in blue, and models that have been falsified are noted in grey.  
Figure 20: MAC-based EDMF results. Experimental values are denoted by red markers, red horizontal lines indicate thresholds, candidates are represented in blue, and models that have been falsified are noted in grey.  
The frequency and MAC-based EDMF produced a narrower range for \(\alpha_{\text{MG1}}\) ([1.20, 1.30]) and \(\alpha_{\text{MG2}}\) ([1.10, 1.30]), compared with the frequency-based EDMF. Both values were comparable and higher than \(\alpha_{\text{OTHER}}\) ([0.80, 1.00]), which is in line with engineering expectations.
# Analytical Attitude Guidance Planner for Multiple Ground Targets Acquisitions
## 5 Simulation and Results  
This section reports the results of the framework proposed in this work. All the tests have been performed using the MATLAB environment on an Intel Core I7-3610 CPU PC with a frequency of 2.30 GHz and 4 GB of RAM. In particular, a run of the _AGP_ algorithm on this machine usually takes about 20 milliseconds. The specific guidance profile explored in the result section depends on the relative position between the satellite and the ground target (as reported in Figure 1). Specifically, the guidance profile depends on the motion of the satellite with respect to the ground target, i.e., it depends on the orbit parameters. The results section only reports one example with four ground targets to show the effectiveness of the proposed strategy. Different results would be obtained with different scenarios, but a more detailed discussion of the relationship between the results and different orbital regimes is beyond the scope of this paper. Moreover, it is worth pointing out that, as aforementioned, the attitude dynamics expression (in Equation (2)) is not simulated, since it is not used to evaluate the _AGP_, but only to derive the restrictions on the angular accelerations from restrictions on the control values (as shown in the Section 2.3). For this reason, the attitude dynamics is not introduced in the simulation environment, but only the orbital and Earth motion are considered as they are the only inputs needed for the _AGP_ to evaluate the time-history of quaternions and angular velocities.  
Simulations are performed to verify and validate the proposed analytical approach. To this aim, the SPICE Toolkit (provided by the California Institute of Technology) is used to generate the simulation environment in terms of orbital and Earth motion with respect to the ECI reference frame. The generation of the attitude guidance as well as the ground points tracked by the optical sensor are evaluated by using MatLab functions written by the authors. Hence, the quaternion guidance is also evaluated by implementing the proposed analytical approach.
## 2 INS/GNSS Concepts
### Inertial Navigation System (INS)
Attitude Update: \[\mathbf{\hat{R}}_{b}^{n}=\mathbf{R}_{b}^{n}(\mathbf{\Omega}_{ib}^{b}- \mathbf{\Omega}_{in}^{b})\] (3) where \(\mathbf{\Omega}_{ib}^{b}\) is the skew-symmetric matrix representing the angular rate of the body frame with respect to the inertial frame expressed in the body frame, and \(\mathbf{\Omega}_{in}^{b}\) is theskew-symmetric matrix representing the angular rate of the navigation frame with respect to the inertial frame expressed in the body frame. These equations allow the continuous update of navigation states based on the inertial sensor measurements. Strapdown systems offer advantages in terms of simplicity and reliability, as they avoid the complexities and potential failure points associated with gimbaled systems, which use mechanical gyroscopes and rotating platforms to stabilize and orient sensors. For a comprehensive treatment of strapdown inertial navigation systems and the associated mechanization equations, refer to [PERSON] [30], [PERSON] [10], and [PERSON] [1].