esa-sceva/satcom-chunk-collection huggingface dataset:
# Ocean Remote Sensing Techniques and Applications: A Review (Part II)
## 2 RS Applications in Ocean
### Sea Surface Temperature (SST)
#### 2.3.1 TIR Radiometer
For example, Landsat-8 has two TIR channels (\(\lambda\) = 10.3-11.3 \(\upmu\)m, and \(\lambda\) = 11.5-12.5 \(\upmu\)m) with a spatial resolution of 100 m, which are very helpful for estimating SST in coastal waters [98]. Moreover, ASTER onboard the Terra satellite acquires images with 14 spectral channels, five of which are TIR channels, with a spatial resolution of 90 m. These datasets are also valuable for accurate SST estimation, especially for coastal areas [98; 100]. MODIS data with four TIR channels (channels 29, 30, 31, and 32) has also been widely used to estimate SST. The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) is also another source to derive high-resolution SST products using five spectral bands (\(\lambda\) = 8.29, 8.78, 9.20, 10.49, 12.09 \(\upmu\)m) with the spatial resolution of 38 m \(\times\) 68 m [98]. The LEO TIR systems have been widely utilized for SST retrieval. For example, [PERSON] et al. [100] developed a statistical algorithm for high-resolution SST retrieval from the TIR channels of ASTER data in the coastal waters of Sendai Bay, Japan. The results indicated that ASTER SST products were independent of the satellite zenith angle. Moreover, [PERSON] (2017) [148] proposed an accurate technique for SST estimation from MODIS data. Their method was based on the incorporation of column water vapor value and the effect of total suspended particulate matter concentration on Sea SurfaceEmissivity (SSE) values. The results indicated that the proposed approach accomplished a decrease in SST estimation error in coastal waters by incorporating the effect of total suspended particulate matter in the estimation of SSE. Finally, [PERSON] (2020) [149] proposed a daytime split-window technique for SST retrieval from MODIS data by incorporating the mid-wave channel/s. The results showed that the proposed method was superior to the physical deterministic SST retrieval scheme. Although the GEO TIR satellite images have coarser spatial resolution compared to the LEO satellites, they provide SST data with higher temporal resolutions (e.g., every 15 min) over a large portion of the globe [14, 99, 150]. Thus, the corresponding SST products are widely used in clear-sky masking to describe the SST diurnal variations [99]. The GEO TIR radiometers provide approximately global SST measurements, missing only high latitudes [99, 150, 151]. For example, the field of view of GOES-16 (in the East position), Meteosat-8 (over the Indian Ocean), and Meteosat-11 (over the Atlantic Ocean), which are processed by the OSI SAF [152] are demonstrated in Figure 7.
## 4 Results and Discussion
### Analysis of Blade Vibration Characteristics under Different Pre-Stresses  
To explore the impact of prestress induced by load generation on the natural frequencies of blades and to prevent resonance during blade operation, this section performs a modal analysis of the blades based on the prestresses generated by the three load scenarios discussed in the previous section. This analysis provides the vibration frequencies of the blades at different loads. Table 2 reveals that with increasing rotational speed, the natural frequencies of the blades under different pre-stresses all increased. When the blades were subjected solely to aerodynamic loads, their natural frequencies were significantly lower than those under centrifugal loads alone or combined loads. This is because the blades undergo certain deformations under aerodynamic loads, but the stresses generated by aerodynamic loads are limited, consistent with the analysis of load effects on blade stress conducted earlier in this paper. By comparing the natural frequencies of the blades under the three types of loads, it is evident that the natural frequencies under combined loads exceeded those under individual loads. However, the natural frequencies under centrifugal loads were very close to those under combined loads, indicating that centrifugal loads are the primary factor influencing the natural frequencies of the blades. When the natural frequency of a blade matches or is an integer multiple of the frequency of the excitation force, it can lead to resonance [11]. The Campbell diagram [20] can be used to determine whether the excitation force frequencies generated by rotating components in a compressor during normal operation coincide with the natural frequencies of some components, effectively preventing resonance. The Campbell diagram is created using modal frequencies at different speeds, lines for different rotational speeds, and excitation force data for the compressor [20]. As shown in Figure 15, the black horizontal lines represent the modal frequency lines for the first six modes of the blade, the red vertical lines represent different rotational speed lines of the impeller, and the blue diagonal lines represent the excitation lines. Since the amplitude of pressure fluctuations increases with lower excitation frequency orders (r), leading to higher vibration stress and increased  
Figure 14: Variation of maximum deformation and maximum equivalent stress of blades at different rotational speeds with mass flow. risk of blade failure [21], this paper considered excitation frequencies corresponding to two, four, six, and eight times the rotational speed frequencies. From Figure 15, it can be observed that resonance points occurred around 62% and 97% of the rotational speed, with a particular resonance point near 97% indicating that resonance is very likely to occur at 100% rotational speed, potentially leading to blade damage. Therefore, it is necessary to either modify the blade structure to change its natural frequency or adjust the compressor's operational speed to avoid the resonance points as much as possible.
## 3 Mathematical Approach Using Homomorphic Encryption and Zero-Knowledge Proofs
### Server Verification with ZKP  
The server also needs to prove that it has performed the computations correctly on the encrypted data. The server generates a proof \(\pi_{S}\) to verify that \(HE.Eval\big{(}Z_{i}^{enc};\theta_{S}\big{)}\) was correctly performed. The server generates a proof \(\pi_{S}\)  
\[\pi_{S}=ZKP.Prove(HE.Eval(Z_{i}^{enc};\theta_{s}),pk_{c})\]  
where \(pk_{c}\) is the client's public key. The client verifies the proof:  
\[ZKP.Verify(HE.Eval(Z_{i}^{enc},\theta_{s}),\pi_{S},\ pk_{C})\ =\ True\]  
If the proof is correct, the client continues the training process.
# Ship Detection from X-Band SAR Images Using M2 Det Deep Learning Model
## 3 Methods  
Figure 2 shows the flow chart of this study, which is divided into (a) an input layer, which indicates an input image and (b) deep learning, in which ship detection learning is performed using a deep learning model. In this study, the SAR image was preprocessed in three different ways to compare the effect on the ship detection result of different training data used during the deep learning model training. An image set generated through each preprocessing is shown in Figure 2a. The first is the SAR intensity image without any preprocessing. In the second, a decibel image was generated by converting the unit of the SAR intensity image values to decibels. Because the intensity image shows a Rayleigh distribution, and the decibel image shows a normal distribution, it was predicted that the decibel image would yield better detection results than the intensity image. Finally, in the third, the intensity difference image and texture image were created. It was expected that the ship detection test results would be improved by enhancing the contrast between the ship and the sea surface through the two images and reducing occasions where other objects are confused with ships. The training data and test data were constructed using images generated by three different preprocessing methods. Figure 2b represents the deep learning model used for ship detection. M2 Det was used as the object detection model for ship detection, and DenseNet [25] was used as the backbone network. By using M2 Det, it was expected that ships of various sizes, from very small to very large ships, would be detected well. Furthermore, by using DenseNet as a backbone network instead of VGGNet [26] or ResNet [27], which are the backbone networks in the existing M2 Det, features of all layers can be used rather than selecting specific layers on the network and extracting features. It was expected that the ship detection performance would be improved by doing this. Furthermore, by using RAdam [28] as the optimization function, stable learning could be performed regardless of the learning rate in all input image sets. After the deep learning model training was completed, the bounding boxes indicating the locations of ships and the confidence score for each bounding box were derived as the detection result.  
Figure 2: Research flow chart. SLC: Single Look Complex. (**a**) Input layers; (**b**) Deep learning model.
## 3 Results and Discussion  
### Electrodes Characterisation in Abiotic Conditions  
The aim of the abiotic characterization was to assess the impact of the different activation procedures on the electrochemical performance of the electrodes. Although the use of the Fe(CN)\({}_{6}\)14\({}^{-/3-}\) pair to evaluate the electrodes activity is not directly transferable to other electrochemical systems (e.g., bioelectrochemical systems), it provides an preliminary estimate of the effectiveness of the electrode [2; 15; 16]. The results from the CV tests (Figure 2) revealed a poor electrochemical performance of the electrochemically activated electrode compared to the DMF- and acetone-activated electrodes. In addition, and in accordance with [PERSON] et al. [12], we observed that the activation procedure greatly affects the electrochemical properties of the material. Our results indicate that, based on the peak potential separation (\(\Delta\)E) of the CV profiles (Figure 2), the acetone activation promotes a faster heterogeneous electron transfer rate than the DMF treatment (\(\Delta\)E = 0.500 V against 0.758 V), probably caused by differences in their electrocatalytic capacity.  
To gain a deeper insight on the impact of the activation method on the electrodes' performance, EIS analyses were carried out using the same redox pair (Fe(CN)\({}_{6}\)]\({}^{4-/3-}\)), with a polarization potential of 0.250 V vs. Ag/AgCl (3.5 M). The resulting Nyquist plots are shown in Figure 3. A characteristic semicircle-like pattern, attributable to the impedance associated with the charge transfer process, is observable for both the DMF and acetone electrodes in the high frequency region (see Figure 3, inset), indicating that the oxidation reaction was kinetically controlled (R\({}_{\text{ct}}\) = 313 \(\Omega\) for DMF and 238 \(\Omega\) for acetone, where R\({}_{\text{ct}}\) is the charge transfer resistance). It is interesting to note that for the electrode activated with acetone, the \"semicircle\" region is depressed, which is probably indicative of pseudo-capacitive behavior [17]. In contrast, the electrode treated with DMF exhibited behavior closer to the ideal capacitive behavior. The electrochemical-activated electrode showed much higher charge transfer impedance, indicating a poor electrochemical performance, which confirms the results obtained in the CV tests.  
Figure 3: Nyquist plots from EIS experiments carried out at 0.250 V vs. Ag/AgCl (3.5 M). The impedance spectra from different pre-treatments can be compared. The inset shows a detail from the high frequency region. The electrical equivalent circuits that fit the EIS data are shown in Figure S2 in the Supplementary Materials (the value of the electrical components is included in Tables S1 and S2).  
Figure 2: Cyclic voltammograms obtained for the different activation methods using the redox couple Fe(CN)\({}_{6}\)]\({}^{4-/3-}\) at 2 mV\(\cdot\)s\({}^{-1}\).
## 3 Smart Inverter Control Architecture
### Inverter Output Voltage and Current Control  
The control scheme of this system adopts the sinusoidal pulse-width modulation (SPWM) technique as the control basis, where the voltage and current control are added to the control process. The overall architecture of the control system is shown in Figure 5 [20].  
To make the inverter output voltage consistent with the mains voltage, after calculating the error between mains voltage \(V_{grid}\) and inverter output voltage \(V_{interter}^{*}\), the output current command \(I_{ac}^{*}\) of AC voltage controller was obtained via the P-I controller, and then added with the current command \(I_{dcbus}^{*}\) generated by the DC-link voltage control to obtain the output current command \(I_{o}^{*}\) of inner loop. This \(I_{o}^{*}\) was multiplied by the unit sine wave (\(\sin\omega t\)) at the same phase with a voltage of mains system connected in parallel to obtain the inverter output current command \(i_{o}^{*}\). Consequently, a comparison with actual current \(i_{o}\) was conducted to obtain errors, where the control signal \(v_{control}\) was received via the P-I controller. Lastly, \(\upsilon_{control}\) was compared with the triangle carrier wave (\(v_{tri}\)) of fixed frequency to obtain the PWM control signal of the switch afterward, which allowed the inverter output current \(i_{o}\) to follow the current reference command \(i_{o}^{*}\). This would allow the inverter output voltage to be consistent with the grid system voltage, and the inverter output voltage would be in the same phase as the current.  
Figure 5 derives each control variable shown in Equations (11)-(14).  
\[I_{ac}^{*}=K_{Pc}(V_{grid}-V_{inverter}^{*})+K_{lv}\int{(V_{grid}-V_{inverter }^{*})dt} \tag{11}\]  
\[I_{o}^{*}=I_{ac}^{*}+I_{dcbus}^{*} \tag{12}\]  
Figure 5: Control architecture of photovoltaic inverter output.  
\[i_{o}^{*}=I_{o}^{*}\sin\theta \tag{13}\]  
\[v_{control}=K_{Pl}(i_{o}^{*}-i_{o})+K_{Ii}\int{(i_{o}^{*}-i_{o})dt} \tag{14}\]  
Among them, \(K_{P0}\) = 0.1 and \(K_{I0}\) = 5 were the proportional and integral constants of the P-I controller for the inverter output voltage. At the same time, \(K_{Pl}\) = 0.15 and \(K_{Ii}\) = 5 were the proportional and integral constants of the P-I controller for the inverter output current.
## 5 Experimental Results
### Performance Analysis for Multiple Cameras Fall Dataset (MCFD)  
#### 5.3.1 Result from Gaussian Module  
We employed Gaussian blur in the module with an optimally chosen kernel size through experimentation to effectively obfuscate the individual's identity. The results are demonstrated in Figure 5. The top row displays frames without blurring, while the bottom row shows frames with blurring. This approach ensures that the video remains sufficiently blurred to the naked eye, making deblurring exceedingly challenging, even with recent advancements in deblurring techniques. Using a large kernel size results in a significant loss of information, posing a substantial challenge for any regenerative deblurring techniques.  
Figure 5: The figure demonstrates frames before (top row) and after (bottom row) the blurring using the Gaussian module. A large Gaussian kernel of size 21 \(\times\) 21 was implemented to obfuscate the video significantly.
# A Performance Analysis of Internet of Things Networking Protocols: Evaluating MQTT, CoAP, OPC UA
## 3 An Overview on IoT Communication Protocols and Frameworks
### Comparison of IoT Communication Approaches
#### 3.8.2 Messaging Pattern  
In terms of the messaging pattern, in the request/response (RR) approach followed by HTTP and CoAP, publishers send data to a server, while consumers request that data from the server. While in the publish/subscribe approach relied upon by, for instance, AMQP or MQTT, publishers send their data to a mediating entity, the broker. The broker distributes that data to consumers that have subscribed a priori the data, via the notion of \"topic\".  
The publish/subscribe strategy is highly relevant from an IoT perspective, given that it provides a way to support asynchronous data exchange and also given that it abstracts, up to some point, the need to handle identifiers of devices.  
Publish/subscribe is being adopted by the most relevant protocols, such as OPC UA. DDS mimics a publish/subscribe approach by relying on IP multicast. As for NDN, it integrates a publish/subscriber receiver-driven and pull-based approach (per packet): consumers first express interest, and then the data are sent by producers, based on the expressions of interest. Therefore, NDN is the only approach that integrates a Publish/subscribe approach truly focused on content and not on the entity (host) that generates such content.
# A Study on Pine Larva Detection System Using Swin Transformer and Cascade R-CNN Hybrid Model
## 4 Implementation
### Comparison between Models According to the Detection Results of Pine Pests  
This study implemented the YOLOv5s model, Faster R-CNN model, Cascade R-CNN model, and the Swin Transformer and Cascade R-CNN mixed model proposed in this paper to detect pine pests, and the performance results were compared and analyzed.  
First, in the YOLOv5s model, as a result of detecting pine pests, the AP (IoU = 0.5) was 56.3%, and the recall value was 66.8%. As a result of detecting pine pests using the Faster R-CNN model, the AP (IoU = 0.5) was 92.5%, the AP (IoU = 0.75) was 87.9%, and the recall value was 91.1%. As a result of detecting pine pests using the Cascade R-CNN model, the AP (IoU = 0.5) was 93.2%, the AP (IoU = 0.75) was 89.5%, and the recall value was 92.9%. In addition, the results of detecting pine pests using the Cascade R-CNN_Swin Transformer mixed model proposed in this paper were as follows: the AP (IoU = 0.5) was 94.6%, the AP (IoU = 0.75) was 89.6%, and the recall value was 93.5%.  
As shown in Table 4, as a result of comparing the performance of the AP (IoU = 0.5, 0.7) and recall of each of the 4 models for detecting pine pests, the YOLOv5s model showed the lowest accuracy and comparing the models, the Cascade R-CNN model and the Faster R-CNN model showed better values, and the mixed model using the Cascade R-CNN_Swin Transformer models showed the highest accuracy.
# Channel and Bit Adaptive Power Control Strategy for Uplink NOMA VLC Systems
## 1 Introduction  
With the rapid development of solid-state lighting technology, visible light communication based on light emitting diodes (LEDs) has attracted a lot of interest in research [1; 2; 3; 4; 5; 6] due to its unique merits such as low cost, large bandwidth, high security, freedom from spectral licensing issue, easy implementation into existing infrastructure, etc. However, multiuser communication support for VLC systems has become a challenging topic due to the limited modulation bandwidth of the LEDs and thus, multiple access technologies have become an hot spot for research.  
For multiple access technologies, it is known that the orthogonal multiple access (OMA) techniques [3; 7; 8; 9] such as frequency division multiple access (FDMA), time division multiple access (TDMA), code division multiple access (CDMA), and orthogonal frequency division multiple access (OFDMA) have been widely studied in recent years in both radio frequency (RF) communications and VLC. However, these OMA schemes allocate the limited frequency, time, and code resources among multiple users and thus, these resources cannot be fully reused [10; 11]. In addition, these OMA schemes cannot guarantee the unique identifiability of each user's signal when users are dense. Very recently, non-orthogonal multiple access (NOMA), as a promising candidate for 5G wireless networks [12; 13; 14; 15], has also been proposed for VLC systems due to the fact the VLC experiences high signal-to-noise ratio (SNR) and the NOMA schemes outperform the orthogonal counterparts in that particular region. Different from these OMA schemes, NOMA enables multiple users to simultaneously utilize all the available frequency, time, and code resources by multiplexing all the users' signals in the power domain at the transmitter.
# Review of Structural Modal Tracking in Operational Modal Analysis: Methods and Applications
## 4 Application of Modal Tracking  
### Application of Modal Tracking in Various Structures  
During long-term structural operation, modal parameters inevitably experience variations induced by external factors such as wind loads, temperature fluctuations, humidity variations, and vehicular traffic (for bridges). These persistent effects lead to gradual deterioration in structural performance, thereby compromising operational safety and reliability. Consequently, employing effective modal tracking methods to precisely characterize the evolution patterns of frequencies and damping ratios throughout the structural lifecycle while elucidating the influence mechanisms of environmental factors constitutes a pivotal component of SHM [124].  
Currently, modal tracking and evolution analysis have been extensively applied to various structural types: (1) long-span bridges, exemplified by the Hardanger Bridge [100], the Sutong Bridge [61], and the Infante D. Henrique Bridge [16]; (2) super high-rise buildings, including the Shanghai Tower [31], Canton Tower [125], the Shanghai World Financial Center [126], and the Ping-An International Finance Center [127]; (3) historical heritage construction, such as the Civic Tower in Rieti [105], San Pedro Apostol Church [106], Milan Cathedral [121], and Santa Maria del Carrobiolo Church [63]; (4) dams; and so on. Detailed applications and modal tracking results are systematically summarized in Table 3. It is worth noting that certain applications in the table, despite tracking the long-term evolution patterns of modal parameters, failed to employ the standardized modal tracking methods outlined in Section 3, which may introduce spurious modes or reduce the automation of the programs.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
## 2 Hybrid Real-Time Scheduler in the MIROS
### Performance Evaluation
#### 2.6.4 Discussions  
Although two schedulers are implemented, the code size of the MIROS does not greatly increase. This is because the MIROS event-driven scheduler only needs to schedule the non-RT tasks while the MIROS multithreaded scheduler only needs to schedule the RT tasks. As a result, the implementations of both the two schedulers can be simplified (Section 2.4).  
The MIROS RT tasks and non-RT tasks are scheduled by two different schedulers. Thus, two kinds of programming styles exist. Commonly, the non-RT tasks need to be programmed by the event-based style (using the phase-split state machine) while the RT tasks need to be programmed by the thread-based style. In order to simplify the programming complexity, the protothread [18] is applied in MIROS. With the protothread, the non-RT tasks can also be programmed in a thread-similar programming style. In this way, the programming differences between these two kinds of tasks can be eased.  
As discussed in the Section 2.3, the MIROS event-driven tasks (non-RT tasks) have the priorities lower than the thread tasks (RT tasks), thus they can be preempted by the latter ones. However, if an event-driven task is preempted by a thread task, and this event-driven task has taken up a resource which is needed for this thread task to continue its execution, the priority inversion problem will occur. To solve this problem in this case, the priority of the event-driven task needs to be improved so that this task can be executed preferentially and release the shared resource that it has taken up quickly.  
Optionally, the MIROS multithreaded scheduler can be developed as a configurable component. If the real-time requirement is not needed by the applications, this component does not need to be built. In this case, the code size of the MIROS can be reduced further.