esa-sceva/satcom-chunk-collection huggingface dataset:
# Transport-Layer Limitations for NFV Orchestration in Resource-Constrained Aerial Networks
## 3 Orchestration in Intermittently Available Platforms
### Intermittent Availability of Control Communications  
As we have previously mentioned, SUAVs battery depletion turns these units into volatile nodes that must be replaced by other units. These disconnections will break links between the SUAVs because they are organized in a multi-hop wireless ad-hoc network, causing communication interruptions between nodes and the GCS. This disruption will be mostly transient however, as either the routing protocols will likely find a new path, and/or the units are replaced after some time have passed to converge into a stable state. Unfortunately, during this period, the units affected may be momentarily unavailable to the MANO system, which will not be able to either deploy or configure VNFs in them, even though those resources will be working properly with their VNFs running normally. Hence, it is important for a VIM to consider that this transient state is the normal behavior on the aircraft, and not interpret these failures as permanent. One possible solution could be supporting a reasonably increased delay when performing orchestration and/or expecting information from the NFVI nodes inside the network. Another possible alternative to support the increased delay could be using new paradigms such as Delay Tolerant Networking (DTN), whose main characteristics consists of storing its data until there is an available hop to reach the next node to the destination (i.e., it is not necessary to have an available path, but rather finding available nodes and forward the traffic until it reaches its destination). Nevertheless, the VIM should adapt to the increased delay on the traffic caused by the network as well, providing a higher window to receive/send updates of the state of the NFVI resources on the devices.  
Moreover, battery depletion is not the only phenomena that can produce these communication failures. SUAVs rely on wireless media for their information exchange, which is a far less reliable medium because it can be affected by a lot of parameters involved during the communication, e.g., weather conditions, distance, frequency used, etc., introducing more link breakdowns and eventual reconnections. This problem can be amplified when mobility is added to the equation, as topology changes and aircraft temporarily being out of range will induce more of those disconnections, causing the temporary unavailability of NFVI nodes in consequence.
## 3 Artificial Intelligence Techniques for 6G Networks
### Cell-Free Massive MIMO Communications  
A cutting-edge strategy used in 6G networks that obfuscates the lines between conventional cells is called cell-free massive MIMO communications. It entails placing several access points (APs) around the coverage region without using specialized cells. These distributed APs work together to provide simultaneous service to many consumers, thanks to AI-driven algorithms. AI improves resource allocation, signal transmission, and interference control by utilizing sophisticated beamforming and spatial processing algorithms. This strategy boosts spectral efficiency, decreases latency, and improves user experiences, making it a potential 6G wireless communication strategy [17].
# When Intelligent Transportation Systems Sensing Meets Edge Computing: Vision and Challenges
## 4 Edge Computing: Opportunities in ITS Sensing Challenges
### Challenges in ITS Sensing
#### 4.3.2 Challenge 2: High Probability of Sensor Failure  
Large-scale and real-time sensing requirements will have little tolerance for sensor failure because it may cause severe problems to the operation and safety of ITS. A representative example is sensor failure in an autonomous vehicle, which could lead to property damage, injuries, and even fatality. When ITS becomes more advanced, where one functional system will likely consist of multiple coordinated modules, the failure of one sensor could malfunction the entire system. For instance, a connected vehicle and infrastructure system may stop working because some infrastructure-mounted sensors have no readings, so the data flow for the system would be interrupted.  
Sensor failure may rarely occur for every individual sensor, but according to probability theory, if the failure probability of one sensor is \(p\) during a specific period, the failure that occurs among \(N\) sensors will be \(1-\left(1-p\right)^{N}\). When \(N\) is large enough, the probability of sensor failure will be very high. This phenomenon is similar to the fault tolerance in cloud computing, which is about designing a blueprint for continuing the cloud computing ongoing work when a few machines are down. However, in ITS sensing, sensor fault tolerance is more challenging due to: (1) the hardware, software, and data heterogeneity mentioned in the last sub-section; (2) the fact that, unlike cloud computing settings, sensors are connected to different networks or even not connected to any network; (3) the potential cost and loss from a sensor fault, which could be much more serious than one in cloud computing.  
This problem naturally exists and is hard to be eliminated, because, as we discussed, even when the failure probability for a single sensor is super low, in city-scale ITS sensing applications, where there are hundreds and even thousands of heterogeneous sensors, the probability goes up significantly. Furthermore, it is not realistic to reduce the failure probability of a single sensor or device to zero in the real world. Edge computing could help improve the situation. On the one hand, an edge computing device or an edge server can make the network edge itself more robust to sensor fault with fault detection designs. A sensor directly connected to the network cannot notify the datacenter of a failure of itself, but when a sensor mounted on an edge device fails, the edge device would know and communicate with the datacenter. On the other hand, backup sensor sets could be deployed within an edge computing platform. With computing capability at the edge, the backup sensor set could be called in case of sensor failure. Nevertheless, more comprehensive solutions to perfectly deal with sensor failure are still under exploration.
# A Survey of Electromagnetic Techniques Applied to Cultural Heritage Conservation
## 4 Deterioration of CH Artifacts: Causes and Process
### Inorganic Materials  
Inorganic does not mean not interacting with living matter, but inorganic natural materials, either raw or obtained through human manipulation, are generally particularly stable from a chemical point of view. Even when subjected to aggressive agents like wind or rain, they preserve their chemical composition despite visible mechanical damage.  
Although considered eternal, stone may be attacked by different kinds of pathogens but, usually, it is the synergistic action between atmospheric agents and microorganisms [94; 95; 99; 100; 95; 101; 102] that deteriorates it. Biodeteriogens take advantage of the stone deterioration produced by wind, water, and other atmospheric agents in terms of cracks and increased porosity in the material. The consequence of the biodeteriogens' metabolic activity is the production of acid, which dissolves the minerals of the stone; this facilitates the degradation produced again by weather events and by the growth of vegetation, and the synergistic cycle keeps going [84; 94].
# Communication Technologies for Smart Grid: A Comprehensive Survey
## Ethernet
### WiMAX (IEEE 802.16)  
Worldwide inter-operability for Microwave Access (WiMAX) is a short range wireless communication technology based on the IEEE 802.16 standards with a data rate up to 70 Mbps and a range of 50 km. WiMAX operates in two frequency bands, 11-66 GHz for line-of-sight, and 2-11 GHz for non-line-of-sight communication [82]. The physical and MAC layers are defined by IEEE 802.16. The physical layer provides Orthogonal Frequency-Division Multiple Access (OFDMA) and Multiple-Input Multiple-Output (MIMO) antenna system providing increased non-line-of-sight capabilities. The Media Access Control (MAC)-layer enables Data Encryption Standard (DES) and Advanced Encryption Standard (AES) encryption to ensure secure and reliable communication. The MAC-layer also enables power saving techniques, such as sleep and idle [83]. WiMAX is scalable and can be set up as networks on local or regional level. WiMAX is well suited for sensors and meters provided sufficient numbers of nodes in the area. One limitation with WiMAX is that coverage becomes highly limited due to signal losses (e.g., rain attenuation, blockage, etc.) [84].
## 4 Antenna Array
### Array with Sequential 90\({}^{\circ}\) Rotation of the Antenna Elements  
In this third configuration, a novel sequential 90\({}^{\circ}\) rotation approach of the array elements is performed. This leads to an element rotation topology where the antenna elements 2, 3 and 4 are respectively 90\({}^{\circ}\), 180\({}^{\circ}\) and 270\({}^{\circ}\) rotated, while the elements 1 and 5 remain unchanged. The manufactured array is shown in Figure 14.  
The geometrical 90\({}^{\circ}\) rotation involves an exchange of the polarization excitation of the antenna elements 2 and 4. This has to be considered, since, for a given array polarization,  
Figure 13: Comparison normal array and array with 180\({}^{\circ}\) rotation @ \(f_{0}\) = 1.325 GHz: normalized co-polar & cross-polar patterns (\(\varphi=~{}90^{\circ}\)). Beamforming.  
different slot arrangements of the antenna elements are used. Electrically, this leads to a different feeding phase from which each antenna is fed, since the coupling slot involved in the antenna elements 1, 3, 5 and 2, 4 is not the same, with each slot being excited by means of microstrip lines with unequal electrical lengths, since they are placed on different substrate heights (Figure 2). Thus, a phase addition has to be applied in order to compensate such electrical length variation of the microstrip lines that feed the slots. The resulting geometrical arrangement of the feeding slots can be seen in Figure 14.  
According to the values of Table 2, lines 1 and lines 2 have a length \(l_{1}\) and \(l_{2}\) of 25.95 mm and 52.3 mm, respectively. As each microstrip line is placed on different FR4 substrate heights \(h_{feed1}\) and \(h_{feed2}\), its guided wavelengths are \(\lambda_{1}=113\) mm and \(\lambda_{2}=128\) mm. Each slot is fed with a phase given by the electrical length of the line from which it is coupled. The electrical lengths \(l_{1}\) and \(l_{2}\) lead to a phase shifting at slot 1 and slot 2, respectively \(\varphi_{1}\) and \(\varphi_{2}\), given by:  
\[\varphi_{1}=(l_{1}/\lambda_{1})*2\pi=83^{\circ} \tag{1}\]  
\[\varphi_{2}=(l_{2}/\lambda_{2})*2\pi=147^{\circ} \tag{2}\]  
That leads to a feeding phase difference between both lines of:  
\[\Delta\varphi=\varphi_{2}-\varphi_{1}\approx 70^{\circ} \tag{3}\]  
The exact phase value is rounded several degrees as simulations show that the precise phase value from which each slot is fed is slightly influenced by the offset of the slot 1, and the position of the slot 2 that is placed above the end of the line 1. This phase difference \(\Delta\varphi\) has to be compensated when exciting the same polarization by means of different feeding slot arrangements.  
In addition, this sequential rotation approach leads to a 180\({}^{\circ}\) rotation of the antenna element 3, as it can be seen in Figure 14, just like it was presented in the previous configuration. This requires an inversion phase feeding of the ports \(H_{3}\),\(V_{3}\), since they have to be fed out of phase.
## 7 Limitations  
Firstly, this study primarily focuses on the emotions and attitudes of the Chinese public, lacking comment data from social media platforms in other countries. This limitation may affect the generalizability of the findings. In fact, significant differences exist among countries in terms of policies, regulations, and public sentiments toward AIGC. Therefore, developing AI education policies tailored to the specific contexts of different countries is a critical issue for future research and policy-making. To gain a more comprehensive understanding of public emotions and attitudes toward AIGC in education across various countries, future studies should expand the sample scope to include more nations and cultural contexts, facilitating cross-national comparative research. Secondly, to further explore the factors influencing AIGC's application in education, future research could adopt more refined quantitative analysis methods, such as empirical studies based on structural equation modeling or experimental designs to validate educational outcomes in real-world settings. These methods could more clearly reveal the key variables influencing AIGC applications in education and their underlying mechanisms. Lastly, future research should strive to provide a more comprehensive, forward-looking, and balanced perspective, thoroughly considering the profound impact of AIGC on the educational landscape and the evolving public emotions and attitudes toward technological advancements. Such efforts would not only offer policy-makers more precise decision-making support but also provide educators and technology developers with more effective guidance, fostering positive interactions and mutual progress between AIGC and education.
## 3 Circuit Implementation
### Voltage-Controlled Buffer  
A VCO buffer is necessary to isolate the VCO and the MSSF, because the sampling capacitor changes the load of the VCO during a complete sampling period if the MSSF is directly connected with outputs of the VCO, which causes considerable spur noise.  
As in Equation (8), \(f_{BW}\) of the proposed SSPLL is determined by the P-path, thus, the bandwidth of the system cannot be adjusted by the pulser as in the conventional SSPLL. The only parameter left to play with in Equation (8) is the amplitude of the VCO buffer. The proposed structure is shown in Figure 7. The input voltage \(V_{CBUUF}\) controls the current source \(M_{5}\) and \(M_{6}\) to change the drive capability of the self-biased inverters so as to adjust the output amplitude.  
Figure 5: Current-shaping in the Class-C region.  
Figure 6: Tuning curves of the proposed VCO. (**a**) coarse-tuning curves, (**b**) fine-tuning curves.  
A series of transient simulations are carried out. Results capture the curve indicating the varying amplitudes as \(V_{CBUF}\) increases, as shown in Figure 8a. A Monte-Carlo simulation is carried out in Figure 8b to show the variation of the output common-mode voltages as the process changes.
# Real-Time Extensive Livestock Monitoring Using LPWAN Smart Wearable and Infrastructure
## 3 Results
### Laboratory Tests  
#### 3.1.1 Energy and Timing Analysis  
The idea behind the programming done in this device is to fine tune parameters to achieve the better range for each use case while maintaining the lowest power figure. Previously we stated that we were using both LoRa and FSK modulations. Additionally, LoRa packets are coded into LoRaWAN frames, that allows an easy deployment in most cases. In these tests we will verify the compromise between speed and power consumption in two different data rates (DR1 and DR5) and FSK modulation. Later, we will check what influences does it have in range if any. Laboratory test setup comprise several sensor nodes all programmed to do the following SmartIMU tasks:  
* First (Figure 7), the sensor samples data from IMU at 12.5 Hz when an event is detected, until desired payload length is achieved. Data are sent through LoRaWAN at DR5, the fastest available at any channel. Then, GNSS receiver is turned on until fix is achieved, and it is sent through LoRaWAN at DR1, the second slowest. We chose DR1 for location data because it let us send data each minute if IMU streaming is turned off, to comply with the duty cycle shows the current while doing a basic SmartIMU task.
* Second (Figure 8), we stream accelerometer data at the highest possible resolution with FSK modulation. This mode is used to define activation thresholds and deciding on the filter constants for the embedded IMU unit registers.  
Our communication model consist of the modes previously explained and which performance is summarized in Table 1. We can observe that each mode is matched to the best modulation for the use case. The best link budget is allowed in GNSS reports, which requires the best sensitivity. In motion reports we value speed over reception, so we chose a faster data rate. For monitoring purposes when we are close to the animal, we can use FSK for the fastest available data rate.  
Figure 7: Current consumption while sampling IMU at 12.5 Hz and activating GNSS receiver.
# A Survey on MLLMs in Education: Application and Future Directions
## 2 Preliminaries of MLLMs
### Key Technologies and Architectures
#### 2.3.2 Multimodal Fusion Techniques  
Information fusion is a critical component in MLLMs, enabling the effective combination of data from different modalities. Fusion methods are typically categorized into the following categories:  
* **Early fusion (single-stream)**: Integrates modalities at the input level, allowing the model to learn cross-modal representations from the beginning. This method processes all modalities simultaneously, enabling the model to capture interactions between different types of data early in the processing pipeline [6].
* **Late fusion (dual-stream)**: Processes each modality separately before combining their representations at a higher level. This approach allows for specialized processing of each modality, which can be beneficial when modalities have very different characteristics or when pre-trained unimodal models are used [6].  
The choice between early and late fusion depends on the specific application and the nature of the data involved. Effective fusion strategies are essential for MLLMs to leverage the complementary information present in different modalities, leading to more robust and accurate models.
## 3 Results and Discussion
### GC Analysis
#### 3.2.2 Semi-Volatile Compounds by GC-MS
The first benzene polycarboxylic acid to become a commercial product was phthalate acid. Phthalic anhydride, the commercial form of phthalic acid, was used in the manufacture of plasticizers, unsaturated polyesters, and alkyld resins and is obtained by the catalytic vapor-phase air oxidation of o-xylene or naphthalene. Terephthalatic acid is used almost exclusively for the manufacture of PET. Another isomeric form, the dimethyl isophthalate, can be also used as monomer in the manufacture of polyesters, as well as the isophthalic acid [30]. Another common starting substance in polyesters manufacturing is adipic acid, which was identified in sample CM2. Several esters of the adipic acid were detected in this sample, but their identification with the libraries was not possible. Related to the PET layer identified in sample CM2, the analytes diphenyl glycol and methyl-(2-hydroxyethyl)erephthalate were detected. Diphenyl glycol was also identified in extracts of multilayer films which contain PET in their composition [31], while methyl-(2-hydroxyethyl)erephthalate is a product formed in the depolymerization of PET into dimethyl terephthalate and ethylene glycol [32]. The compound terephthalatic acid ester of neopentyl glycol cyclic dimer was also identified in all the samples. These short chain cyclic polyesters can be considered as NIAS and, therefore, a chemical standard is not commercially available to carry out safety evaluation studies [2]. Dimethyl adipate and dimethyl maleate, identified in sample CM2, can be used as monomers in the synthesis of polyesters via polycondensation [33], while methyl palmitate, detected in all the samples, is used as intermediate for detergents, emulsifiers, stabilizers, belizers, resins, lubricant, plasticizers, and defoamer in food-contact coatings [34]. 2,6-diisopropylnaphthalene, present in samples CM1 and CM2, is used for making monomers of high-performance polyester fibers, molded plastics, thermotropic liquid crystalline polymers, and other advanced polymer materials [35]. Several benzaldehyde compounds were identified in sample CM1, such as 2,5-dimethyl-4-methoxybenzaldehyde, 2-hydroxy-5-methylisophthalaldehyde, and 4-hydroxy-3,5-dimethylbenzaldehyde, which were also identified in the polyester coating analyzed by [PERSON] [36].
## 4 Experimental Evaluations
### Case 2: The Kurtosis Time Series with STPs  
Figure 7a shows the kurtosis curve bearing 1 at incipient fault stage, from the 546 th point to 945 th point. The Hurst exponent is also computed via the R/S approach, as shown in Figure 7b. The resulting \(H=0.9640\), which is much larger than 0.5, and \(d=H-0.5=0.4640\) indicates that the kurtosis time  
Figure 6: The EVI prediction results by the proposed method and the other state-of-the-art methods. (**a**) Prediction result using the autoregressive–moving-average (ARMA) model; (**b**) Prediction result using the fractionally autoregressive integrated moving average (\(f\)-ARIMA) model; (**c**) Prediction result using the fractional Brownian motion (FBM) model; (**d**) Prediction result using the proposed model; (**e**) Prognostic errors.  
series have a strong LRD property. However, it should be noted that some STPs are contained in this kurtosis time series (here is historical region, which means the region is formed by the points 546 th to point 945 th), which illustrates the uncertainties of the predicted data.  
In this experiment, 400 kurtosis points (from the 546 th point to 945 th point) are used as the historical-based data to predict the 40 points; i.e., 400 mins in advance. The prognostic results and prognostic errors with the proposed method and other methods are displayed in Figure 8. It is observed that the prediction values by the \(f\)-ARIMA and FBM models gradually deviate from the actual values, especially in the last 15 points (some STPs are contained in the predicted region), which shows that the FOC models have failed when the STPs are contained in the predicted region. By comparison, the proposed approach performs better in multi-step prediction than the existent ARMA model and the FOC models. The specific parameters for quantitative assessment can be found in Table 15 in Appendix A. Overall, the above experimental results verify that the improvement of the proposed approach in terms of the weak-LRD property and STPs time series, which is able to provide a more accurate prediction for the rolling bearing degeneration.  
Figure 8: _Cont._  
Figure 7: Kurtosis time series and its Hurst curve. (**a**) kurtosis time series with sharp transition points; (**b**) Hurst curve.