esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Small-Scale Vapor Compression Refrigeration Technologies
## 2 Small-Scale Refrigeration Technologies
### Active Technologies
#### 2.2.4 Thermoelectric Cooling  
Thermoelectric cooling is generated based on the Peltier effect, by converting electricity into a temperature difference. A thermoelectric cooler consists of two thin ceramic wafers that enclose a series of N-P-type semiconductor material junctions between them. When a DC current is supplied, one of the ceramic plates cools down, while the other warms up, thus generating a temperature difference between them. Usually, two additional heat sinks are mounted on these ceramic plates to enhance heat transfer, forming a thermoelectric module. Figure 5 shows the components of a thermoelectric cooler.  
Thermoelectric devices are used for heating or cooling, power generation, and as sensors. Cooling applications include small refrigerators, electronics cooling, air conditioning, vehicle refrigerators, picnic coolers, motorcycle helmet coolers, and other uses in medical, marine, and aircraft applications [34].  
An important issue with thermoelectric cooling is their low coefficient of performance, COP, limiting this technology to applications that need relatively low cooling capacity. Therefore, hybrid systems have been studied and the results show that a high heat flux of the order of 0.5-1 \(\times\) 10\({}^{7}\) W/m\({}^{2}\) can be dissipated [35].
## 3 Results and Discussion
### Characterization
#### 3.1.1 SEM Study  
Surface morphology and average size diameter of synthesized materials were investigated through SEM. Figure 1a-c shows SEM images of Fe\({}_{3}\)O\({}_{4}\), PANI, and PANI/Fe\({}_{3}\)O\({}_{4}\) respectively before adsorption. Iron oxides have a spherical morphology and an average particles size of 0.07 \(\upmu\)m (70 nm) and are highly porous. It can be observed that these particles are agglomerated to each other which may be due to van der Waals forces. Existence of such forces has been observed in the earlier work [43]. PANI shows cabbage leaf like morphology, in which the polymers chains are adhered to each other through hydrogen bonding. After formation of composites with Fe\({}_{3}\)O\({}_{4}\) particles, the morphology of PANI becomes sponge like and highly porous. Although visibility of Fe\({}_{3}\)O\({}_{4}\) particles in the composites is not clear due to PANI layer formation, on close observation some bright and dark images of the core-shell like microsphere composites structures can be seen in the composite texture [44]. After adsorption of Cr(VI), the surface morphology of Fe\({}_{3}\)O\({}_{4}\) slightly changes from spherical to rough due to interactions of Cr(VI) with active sites on the surface of Fe\({}_{3}\)O\({}_{4}\). Figure 1d shows that the average diameter of the Fe\({}_{3}\)O\({}_{4}\) particles increased to 0.18 \(\upmu\)m (180 nm) as reported elsewhere [45]. The visibility of sphericalness became clearer when Ni(II) adhered onto Fe\({}_{3}\)O\({}_{4}\). The average diameter of Fe\({}_{3}\)O\({}_{4}\) particles increased to 0.09 \(\upmu\)m (90 nm) after adsorption of Ni(II). Figure 1e,h shows SEM images of PANI after removal of Cr(VI) and Ni(II) respectively. As mentioned above, PANI showed cabbage leaf like morphology before adsorption which changed into cauliflower like shape when Cr(VI) and Ni(II) ions came in contact with the surface of PANI. Similarly cloudiness in the morphology of composites after adsorption of Cr(VI) and a cluster of ball like Ni(II) particles appeared after their adsorption as shown in Figure 1f,i. Similar results were observed in an earlier work where polypyrrole was used as an adsorbent to remove nickel from aqueous solution [46].
# Improved Solar Operation Control for a Solar Cooling System of an IT Center
###### Abstract  
In this contribution, a model predictive control algorithm is developed, which allows an increase of the solar operating hours of a solar cooling system without a negative impact on the auxiliary electricity demand, e.g., for heat rejection in a dry cooler. An improved method of the characteristic equations for single-effect H\({}_{2}\)O/LiBr absorption millers is used in combination with a simple dry-cooler model to describe the part load behavior of both components. The aim of the control strategy is to find a cut-in and a cut-off condition for the solar heat operation (SHO) of an absorption chiller cooling assembly (i.e., including all the supply pumps and the dry cooler) under the constraint that the specific electricity demand during SHO is lower than the electricity demand of a reference cooling technology (e.g., a compression chiller cooling assembly). Especially for the cut-in condition, the model predictive control algorithm calculates a minimum driving temperature, which has to be reached by the solar collector and storage in order to cover the cooling load with a low cooling water temperature but restricted auxiliary electricity demand. Measurements at a solar cooling system for an IT center were used for the testing and a first evaluation of the control algorithm.  
solar fraction; minimum driving temperature; model predictive control; absorption chiller; dry cooler; characteristic equation method +
Footnote †: journal: Applied Sciences
## 3 Proposed Scheme to Secure Device Layer Network  
M2M systems consist of a combination of connected systems and devices. To deal with M2M system architecture, we consider the overall architecture (Figure 1) given in ETSI Technical Report [1].  
The general M2M architecture is divided into three interconnected domains called the M2M domain, the network domain and the application domain. The main constituents of this architecture can be described as follows:  
- M2M devices for collecting and transmitting requested data.  
- The M2M device network connecting the M2M devices to M2M gateways.  
- M2M gateways interconnecting M2M device networks to communication networks.  
- M2M communication network connecting M2M gateways to Internet or to the M2M application servers.  
- M2M server providing data and ensuring different services from various M2M applications.
## 2 Methodology
### Samples
#### 2.2.2 Customized Sectioned Sample  
After the different coating layer thicknesses were measured, we created a customized sample which was divided into four regions containing metal and corroded metal with and without a protective coating layer (sample 7, Figure 2).  
In order to prepare the desired sample, we first corroded the complete sample artificially by embedding the metal into a natrium chloride bath and exposing it in a climatic chamber afterwards. In more detail, the sample was created following the procedure described in [36]:  
1. Embedding in a 5% NaCl-solution
2. Exposure to 100% relative humidity for 8 h at 40 \({}^{\circ}\)C
3. Drying for 16 h at room temperature
4. Repetition of steps 1 to 3 five times
5. Exposure to outdoor weathering for 2 months inclination 45\({}^{\circ}\), south-side exposure
6. Desalination by immersion with distilled water  
Figure 2: Microscopic image of the customized sectioned sample: (**a**) pure metal; (**b**) pure metal with coating layer on top; (**c**) corroded area; (**d**) corroded material with coating layer on top.  
Soft air abrasive cleaning with walnut shells (pressure 8 bar, distance 50 cm)
* Additional air abrasive cleaning for half of the sample (upper part) until bare metal was reached.
* Application of coatings by brush, five to six repetitions (resulting layers), intermediate drying period of 3-4 h for each layer.  
To avoid infiltration of the coating, a sellotape stripe was glued before applying the Paraloid B48N onto the sample. However, even by the use of a sellotape stripe, some infiltration zone could be observed.
# Controlling Remotely Operated Vehicles with Deterministic Artificial Intelligence
## 4 Discussion
### Findings and Their Implications  
The controller plays a crucial part in the operation of an ROV. At its core, the controller's role is to translate the operator's instructions into actual vehicle motion, so the operator's ability to complete an objective is directly tied to the quality of the ROV's controller. The controller outlined in this paper provides a framework that presents a number of advantages to both operators and manufacturers over classical controllers typically used for ROV control. One advantage that this controller presents is the high degree of tracking accuracy. The controller presented here achieved milli-degree tracking accuracy within seconds of startup. This level of accuracy makes the controller well suited to extremely sensitive tasks which require a high degree of precision such as sampling delicate marine life or explosive ordnance disposal (EOD). The adaptive nature of the controller also makes it well suited to applications where the system dynamics may change unexpectedly. Again, EOD operations present situations where the ROV may be subject to significant damage, and the adaption could allow recovery and mission completion after sustaining damage that would otherwise cripple the vehicle. The adaptive nature of the controller also means that it required no tuning. Tuning can be an expensive part of ROV design, often requiring significant time from trained engineers. Removing the requirement for tuning presents manufacturers with an opportunity to reduce the cost to produce an ROV.  
The approach is validated to effectively control the remotely operated vehicle with increasing accuracy with the passage of time. The application of deterministic artificial intelligence in autonomous unmanned vehicles (UUVs) proved to be effective with no design tuning required, and that feature appears to be true for the disparate system equations of the Seabotix vLBV 300 remotely operated vehicle. The working hypothesis is proved by results that are comparable to the previous studies on UUVs.
## 3 Results and Discussion
### Fatty Acids Content  
By GC-MS analyses of the dried spirulina extract, five fatty acids were detected and identified (Table 3). The saturated fraction (66.0%) exceeded the unsaturated one (34.0%). Palmitic acid (64.1%), linoleic acid (13.7%) and oleic acid (10.0%) were the most abundant followed by \(\gamma\)-linolenic (6.2%), palmitoleic (4.1%) and stearic acid (1.9%).  
Our data partially agree with those of [PERSON] et al. [36], which reports palmitic, \(\gamma\)-linolenic and linoleic acids as the most abundant ones. It has also been hypothesized that fatty acid composition may be useful for differentiating different spirulina strains. [PERSON] et al. [37] carried out GC/MS analysis of the methanolic extract of _A. platensis_ from Egypt and they revealed the presence of fatty acids such as hexadecanoic acid, 9,12,15-octadecadienoic methyl ester, and linolenic acid ester. Secondary phytochemical analysis carried out on the methanolic and petroleum ether extracts of spirulina from India, revealed the presence of myristic and palmitic acid and docosane 9-Octadecenal [38]. In another study, 37 varieties of spirulina available on the market and collected in different countries, such as the USA, Canada, Australia, the UK, India, New Zealand and Malaysia, were analyzed for comparative purposes, using gas chromatography and liquid chromatography to evaluate the presence of fatty acids, amino acids, sugars and polyphenols. The results demonstrated that the content of polyunsaturated fatty acids, compared to the other classes of compounds detected, was the one that varied most among the spirulina samples investigated [39].  
The levels of fatty acids can be influenced by environmental factors; in fact, a previous study reported how, albeit with minimal variations, the content of fatty acids increased with increasing cultivation temperature, but the relative quantity of polyunsaturated fatty acids (PUFA) decreased [36]. Fatty acids are essential in the diet and can partially satisfy nutritional needs. Furthermore, they exert pharmacological effects and can be useful in the management of some diseases. In particular, polyunsaturated fatty acids (PUFA) play a key role in improving the immune function and reducing systemic inflammation [40].
## 1 Introduction
Traditional Proportional-Integral-Derivative (PID) control is only effective under small disturbance conditions with relatively low control precision [30]. The typical disturbance rejection methods, which is used widely in practice, include adaptive control, robust control and sliding model control [31; 32]. These control approaches generally achieve disturbance rejection via feedback regulation based on the tracking error between measured outputs and set-points. Thus, they cannot react directly and fast enough in the presence of strong disturbances, although they can finally suppress the disturbances in a relatively slow way [33]. Due to limited shifting duration, an overcomplicated control algorithm is obviously not desirable. The two-degree of freedom controller based on a disturbance observer [10; 34] has a simple structure and provides the ability to perform both set-point tracking and disturbance rejection. The extended state observers (ESO) technique was first proposed as part of active disturbance rejection control (ADRC) [35]. The main advantage of the ESO technique is that it can estimate uncertainties in the system with little model information, and it takes parameter uncertainties and external load force as the lumped disturbances [36; 37]. With the available state, the reduced-order ESO [38] could be designed to simplify the structure of the observer. In this paper, a velocity estimator is proposed to achieve sensorless control of independent gear-shifting actuator. A model-assisted reduced-order ESO based cascade control strategy is designed to improve both the robust stability and the transient performance of position control. The rest of paper is organized as follows. The system overview and the models of LEMA are introduced in Section 2. In Section 3, the detailed design procedure of the velocity estimator is explained. A model-assisted reduced-order ESO based cascade controller is proposed in Section 4. In Section 5, comparative simulations and experimental tests are conducted to verify the effectiveness of the proposed method. Finally, the paper is concluded in Section 6.
## 2 Theoretical Model
### Numerical Resolution  
Equations (1b), (2), (3b), (4b) and (5), coupled with the initial and boundary conditions, describe the dynamics of the bubble formation process. They can be solved by means of a finite-difference method.  
To solve the nonspherical bubble formation model numerically, the following algorithm of the finite-difference procedure is used:  
1. The operating conditions, the initial time step, and the initial surface point number are defined to form the parameter initialization.
2. Provided that the bubble does not detach and the gas chamber volume does not change, the air flow rate through the orifice \(v\) at time \(t\) can be given by the following expression: \[v|_{t}=\mu\sqrt{\frac{2\big{(}P_{g}\big{|}_{t}-P_{b}\big{|}_{t}\big{)}}{\rho_{ g}}}\] (6) The pressure in the gas chamber at time \(t\) is obtained from an integral equation: \[\frac{dP_{g}}{dt}=-\frac{\pi P_{g}D^{2}v}{4V}\] (7) \[P_{g}\big{|}_{t}=\frac{P_{g}\big{|}_{t-\Delta t}-\Delta t*\pi D^{2}*P_{g} \big{|}_{t-\Delta t}*v|_{t-\Delta t}}{4V}\] (8) The pressure inside the bubble at time \(t\) can be given by the following expression: \[\frac{dP_{b}}{dt}=\frac{\pi D^{2}v}{4}\] (9) \[P_{b}|_{t}=\frac{P_{b}|_{t-\Delta t}+\Delta t*\pi D^{2}*v|_{t-\Delta t}}{4}\] (10) The volume inside the bubble can be expressed as follows: \[\frac{dV_{b}}{dt}=\frac{\big{(}P_{g}-P_{b}\big{)}\pi D^{2}v}{4P_{b}}\] (11)\[V_{b}|_{t}=\frac{V_{b}|_{t-\Delta t}+\Delta t*\left(\left.P_{g}\right|_{t-\Delta t }-\left.P_{b}\right|_{t-\Delta t}\right)*\pi D^{2}*\left.v\right|_{t-\Delta t}}{4 \left.P_{b}\right|_{t-\Delta t}} \tag{12}\]
3. The pressure in the gas chamber is compared with the pressure in the bubble at time \(t\) calculated in step (b). If the error is more than 10 Pa, it can be considered that the bubble does not detach, and the procedure begins again at step (a) with \(t+\Delta t\); otherwise, the bubble detaches, and the procedure continues on to step (d).
4. The bubble detaches. The variation in all parameters as a function of growth time are given and recorded.  
In this calculation, the experimental values of the defect size and the elastic pressure, which can be determined from experiments and has been previously referred to in the literature, are used as the only two inputs to solve the equations for each step time of bubble formation to predict the pressure change in the gas chamber. The set of first-order differential equations can be solved in Matlab ((R2014a, MathWorks Inc., Natick, MA, USA and 2016) using the 4 th order Runge-Kutta method.
# Move Over Law Compliance Analysis Utilizing a Deep Learning Computer Vision Approach
## 3 Methodology
### Video Data Processing-Object Detection and Tracking
#### 3.2.3 Move Over Compliance Detection  
Move Over compliance detection is based on vehicle position analysis with respect to a particular lane and the distance to a vehicle at a roadside incident. The algorithm's logic is centered on three related classifications:  
* Determining if a vehicle is subject to the Move Over law;
* Determining if the vehicle moved over, given that it was judged to be subject to the Move Over law;
* Determining if the vehicle slowed down, given that it was judged to be subject to the Move Over law.  
Move over laws vary from state to state, and at this stage of research, we are still not sure how to address these differences. Among the currently examined options the most promising ones seem to be the following:  
* Assume that everyone should Move Over (ignore the slow-down analysis).
* Assume that slowing down is enough to comply with Move Over laws. In this case, we also need a slow-down threshold that determines whether the vehicles slowed down sufficiently. Such a threshold can be an absolute value (e.g., a vehicle slowed down by at least 20 mph) or relative (e.g., a vehicle slowed down by at least 20%).
* Perform additional analyses that try to determine if it was possible to Move Over. Such analyses may be based on the particular scene characteristics (e.g., sufficient space on the adjacent lane) or on traffic volumes (e.g., if traffic volume is not excessive, we may believe that it is always possible to move over).  
Bullets two and three require additional assumptions. Moreover, exploratory data analysis shows that in the majority of cases, the traffic was not close to capacity (in 94.6% of scenes the traffic was below 2000 vehicles/lane/hour), so it was probably usually possible to move over. Thus, in this study, we decided to assume that the vehicles comply with the Move Over law only if they physically moved over (changed a lane). Specifically, we defined a lane change as compliance only when the center of the vehicle fully shifted into an adjacent lane. Partial lane changes, such as slight drifting or straddling lane markers, were not considered compliance to ensure objective and consistent evaluation.
# A Comprehensive Review of Multimodal Analysis in Education
## 4 Applications in Education
### Multimodal Assessment and Personalisation  
Studies in this category aim to personalise educational experiences and improve assessment practices through multimodal evidence. By combining diverse data sources--such as eye tracking, audio, video, written reflections, and physiological signals--these approaches seek to construct holistic learner models that support adaptive feedback, early detection of learning risks, and tailored interventions. This line of work highlights the potential of MMLA to support formative and stealth assessment strategies in a range of disciplines.  
For instance, [PERSON] et al. [7] investigated the use of multimodal classroom data to support teacher reflection, integrating physiological signals, speech, and classroom interaction logs. Their study identified critical factors that influence teachers' ability to reflect on their practices, including the need for timely, context-rich feedback that aligns with teachers' privacy concerns and preferences for data sharing. Similarly, [PERSON] et al. [69] developed a multimodal teaching quality prediction model that combines complex network analysis andattention mechanisms to predict teaching outcomes. This approach achieved a high level of accuracy by integrating multimodal data such as grades, classroom behaviour, and psychological characteristics, demonstrating the potential of complex network structures for personalised education.
## 3 Materials and Methods
### Hop Cone Analysis
#### 3.3.2 Hop Cone Electronic Nose Analysis  
The electronic nose employed for the analysis was the Cyranose 320 (Sensigent LLC, 1438 Arrow Hwy, Baldwin Park, CA, USA, 91706). The Cyranose 320 integrates 32 polymer sensors, which, when in contact with a vapour, absorb the vapour and swell like a sponge. During this process, the electronic nose arrays are used to identify the vapour [37]. The ability of the electronic nose to discriminate different samples has been previously observed, for example, in long-grain rice, where it was able to discriminate the cultivars [37]. In another study, the electronic nose was successfully used with apples to distinguish fruit ripeness grades [38; 39]. In our study, the electronic nose was used to analyse and characterise the samples. Cyranose 320 software was used to record the experimental data. Six dried cones per sample in triplicate were placed into a 50 mL falcon, which was then closed with the internal headspace equilibrated for 30 min. Measurements could be registered when the resistance of the gas sensors remained stable at a high value of 0-1 [37].  
Before modelling, the cross-validation technique was applied to determine the optimum number of principal components. The results were used in principal component analysis in order to classify the samples and identify differences.
## 3 Results
### Optimization of AgNPs Synthesis
#### 3.2.2 The Influence of Plant Extract:AgNO\({}_{3}\) Ratios on AgNPs synthesis  
In order to determine the optimal ratio for AgNPs-H\({}_{2}\)O formation, three variations of the ratio between the _T. erecta_ extract and the AgNO\({}_{3}\) solution (1:9, 5:5 and 9:1) were tested.  
For the first sample, 1 mL of extract was brought to a pH of 8 using a 0.1 M NaOH solution, then 9 mL of 3 mM AgNO\({}_{3}\) was added. For the second sample, 5 mL of extract was brought to a pH of 8 and 5 mL of 3 mM AgNO\({}_{3}\) were added, while for the last sample, 9 mL of extract was brought to a pH of 8 and 1 mL of 3 mM AgNO\({}_{3}\) was eventually added. The three samples were left under magnetic stirring for one hour and then the absorption spectra were recorded (Figure 3).  
The characteristic peak for AgNPs formation appeared at 410 nm for the 1:9 plant extract:AgNO\({}_{3}\) ratio, which was further used for the proper synthesis.  
Figure 1: The influence of different AgNO\({}_{3}\) concentrations (1 mM, 3 mM, 5 mM, and 10 mM) on AgNPs-H\({}_{2}\)O formation.  
For AgNPs-EtOH formation, the same procedure was used for the samples. After one hour of magnetic stirring, the absorption spectra were recorded (Figure 4).  
Out of the three investigated variations of the volume ratios, the peak corresponding to the formation of AgNPs-EtOH formation was more conclusive in the case of the 5:5 plant extract:AgNO\({}_{3}\) ratio.