esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion on Ethical Aspects  
The implementation of the Victim ID system raises important ethical issues that must be considered to ensure its responsible use. These issues primarily concern the handling of confidential and sensitive data and potential bias, and the necessity of maintaining accountability in decision-making processes.  
Given the sensitive nature of postmortem and antemortem data, ensuring data privacy and security is crucial during system implementation. The system was designed with mechanisms such as WPA2-PSK encryption and image watermarking to protect against unauthorized access and misuse. However, these measures must be combined with strict adherence to data protection regulations and the establishment of clear policies regarding data retention, access control and accountability [56]. Such safeguards are essential to minimize the risk of attacks on the system, which is expected to manage a substantial database of sensitive information [57]. Furthermore, ongoing audits and regular updates to security protocols should be integral to the system's operation to address evolving threats and maintain trust [58].  
The error analysis so far has shown that the system, especially in the initial stage after implementation, may make false predictions across demographic groups such as gender and ethnicity. This highlights the need to feed the system with diverse and representative training datasets to minimize bias [59]. Regular updates to training data and fine-tuning of the model are essential to ensure its continued accuracy and inclusivity, particularly as new real-world data become available after implementation.  
In addition, the implementation of AI algorithms in sensitive areas like disaster victim identification and terrorist attacks requires that the transparency of the system does not raise any objections. Decisions informed by the system's operations can significantly impact individuals or their families, making it vital that the system's processes are fully understandable and traceable [60]. To achieve this, the implementation of detailed registers and audit trails is crucial. These records will document user interactions with the system, including data inputs, intermediate processing stages, and final results. This will allow for a comprehensive review of the decision-making process and, if necessary, will facilitate the identification of the source of errors.  
Moreover, the system is designed to automate and expedite many aspects of the identification process, and final decisions must always remain under human supervision. Trained professionals should use the hypotheses generated by the system, but should weigh them against their own expertise and contextual understanding. The system is therefore supportive but not a substitute for human judgment.
###### Abstract  
A commonly adopted dehumidification system on a suspension bridge is the desiccant wheel dehumidification system (DWDS), which demonstrates ineffectiveness and energy-intensiveness in high temperature and humidity scenarios. This paper proposes a suspension bridge hybrid dehumidification system (HDS) as a better alternative for corrosion protection. A numerical model of HDS was first established. Then, the effects of the main operating parameters on HDS were analyzed, and the dehumidification performance of HDS and DWDS was further compared to illustrate the superiority of HDS to apply on a suspension bridge. In addition, the air supply parameter was discussed, and a low-energy operation strategy of HDS in summer cases was proposed. Finally, limitations and adaptations of heat pump dehumidification system (HPDS) and DWDS on suspension bridges were discussed. The results showed that: (1) HDS realizes the utilization of waste energy from suspension bridges, enhancing the system efficiency. Its specific moisture extraction rate (\(SMER\)) reaches 3.16 kg kW\({}^{-1}\) h\({}^{-1}\) in a high-temperature and -humidity environment (35 \({}^{\circ}\)C, 30.82 g kg\({}^{-1}\)) of the suspension bridge. (2) In the same inlet air conditions, HDS has greater dehumidification capacity than DWDS, and this advantage is enlarged with the increment of inlet air temperature and moisture content. In addition, HDS can strengthen dehumidification ability by decreasing the evaporation temperature and increasing the regeneration temperature to meet peak moisture loads of the suspension bridge. (3) Considering the anti-corrosion effects, energy consumption and drying time, the authors recommend that the moisture content corresponding to the atmospheric temperature and RH of 45% be used for air supply on a suspension bridge. (4) HPDS has poor adaptability to temperatures below 20 \({}^{\circ}\)C, while DWDS has poor adaptability to some high temperatures of 24-40 \({}^{\circ}\)C and high humidities of 19-30 g kg\({}^{-1}\). None of them can meet the air supply requirements of a suspension bridge's main cable alone.  
s 2023 13 4219 1
# Performance of Cooperative Relay NOMA with Large Antenna Transmitters
## 3 Achievable Rates, Outage Probabilities and Bit-Error-Rates Analysis  
### Transmission between TX and RS  
Here, we consider a two-hop downlink communication between a TX and two UEs through a dedicated RS and apply the 3D GBSM channel to the TX\(-\)RS and RS\(-\)UEs communication links.  
We analyze the transmission between TX and RS in the proposed 3D GBSM mMIMO CR-NOMA system model [103; 104; 105] with large antenna transmitters such as URA or CA in Figure 5, and as illustrated in Figure 3. Two key assumptions are taken into account in the system model. Firstly, there is no direct communication link between TX and UEs. Secondly, users are close to each other in a half-duplex communication mode and have a single antenna each for reception. The communication between TX and UEs takes two stages. During the first stage, the TX transmit a superposition of two distinct signals (**x**) to the relay, from which the relay re-transmits to all UEs. The received signal (\(y_{RS}\)) at the relay can be expressed as  
\[y_{RS}=\sqrt{\alpha_{R}}\mathbf{h}_{TX}\mathbf{x}+\eta_{R} \tag{16}\]  
where \(\mathbf{h}_{TX}\) is the proposed 3D GBSM [103; 104; 105; 106] channel coefficient between the TX and RS, \(\eta_{R}\) is the additive white Gaussian noise (AWGN) with unit variance. \(\alpha_{R}=d_{SR}^{-\delta}\) is the distance-dependent path loss, where \(d_{SR}\) is the distance between TX and RS, and \(\delta\) is the path loss exponent. Again, using the concept of NOMA, users are arranged in ascending order according to the power allocation factors such that \(\alpha_{1}<\alpha_{2}\), where the superimposed signal (**x**) is given in (1).
# Recent Advancements in Non-Destructive Testing Techniques for Structural Health Monitoring
## 8 Acoustic Emission Technique
### Masonry Structures  
Despite the fact that SHM of masonry structures is an engineering challenge, [PERSON] et al. [99] proposed an innovative procedure of using AE for assessing the damage. A uniaxial compression test was conducted, and two AE sensors were used on each side of the specimen close to the mortar joint; a thin layer of Vaseline was applied between the sensor and the wall to allow for sufficient acoustic coupling. Initial spectrum analysis was carried out, and an amplitude-frequency diagram through FFT transformation was developed in order to filter signal measurements and eliminate environmental interference. The AE parameters were assessed, and a linear between AE cumulative events and damage degree was suggested. The application of AE in masonry structures has also been investigated by [PERSON] et al. [100]. Pencil-lead breaks which are widely used as a reproducible source for test signals in AE applications have been used in the study of [PERSON] et al. [100]. Six sensors were installed at increasing intervals on a masonry model bridge to analyse wave velocity (see Figure 12b). On the basis of AE readings, the traditional procedure applied for crack source localisation in concrete has been modified for masonry structures, and a crack characterisation approach with good reliability was proposed.
## 2 Autoencoder for Normal Image Reconstruction
### Structure of Encoder  
The encoder architecture designed in this paper is shown in the encoder part of Figure 4. The encoder extracts and encodes image features in the latent space. The depth of the model will affect whether the learned feature maps are discriminative. However, a model that is too deep will make it difficult to retain image details, resulting in blurred reconstructed images, and even overfitting. Therefore, the design of the model must strike a balance between depth and detail preservation.  
The encoder first applies a \(1\times 1\) filter to the input image to increase the depth from 3 to 16 to increase the feature capacity in the first layer. Then, downsampling and feature extraction submodules are combined to form a basic module in the encoder, and this module is repeatedly used to generate the Latent feature vectors. In addition, in the downsampling process (dimensionality reduction), while the length and width of the feature map are reduced by half, the depth is doubled to avoid excessive feature-loss to maintain the image detail. Finally, the \(1\times 1\) Conv2d module is used to generate latent vectors.  
The two submodules, feature extraction and downsampling are the core of the encoder, and their architecture can be modified according to the image complexity. In this study, we design the simple convolution module and Inception convolution module as the feature extraction modules with different complexities. The feature-extraction module can be constructed with a simple convolution module, shown in Figure 5a, by using only one layer of the Conv2d module, or refer to the architecture of Inception v4 convolution module [27], shown in Figure 5b. Compared with the simple convolution module, the Inception convolution module is composed of four different branches designed to capture features across different scales.  
The main function of the downsampling module is to decimate the size of the feature map and increase the depth to twice the original. Figure 6a shows a downsampling module with a relatively simple structure. For reasons of preserving image details, the Inception downsampling module shown in Figure 6b uses three branches to reduce dimensionality in different ways, and finally, outputs of summation of the feature maps.  
Figure 5: (**a**) Simple convolution module. (**b**) Inception convolution module.  
Figure 6: (**a**) Simple downsampling module. (**b**) Inception downsampling module.  
The Conv2d module used in the above-mentioned feature extraction and the downsampling modules is designed as the structure shown in Figure 7, which includes the convolution, batch normalization, and Relu activation operations.
# Single-Phase, Bidirectional, 7.7 kW Totem Pole On-Board Charging/Discharging Infrastructure
## 2 Brief Detail and Parameter Design
### Output or DC-Link Capacitor Design  
Similarly, the output capacitor (\(C_{DC}\)) is the main energy-storing element. Simultaneously, it is, relatively, the most expensive and bulky element of the PFC. Therefore, its sizing should be optimum so it can meet the specifications of the system. Mainly, the value of the output capacitor is determined considering the output current ripple (\(\Delta I_{DC-link}\)), the allowable voltage ripple (\(\Delta V_{DC-link}\)), and the required hold-up time (\(t_{hold}\)). Among these, \(\Delta I_{DC-link}\) and \(\Delta V_{DC-link}\) are considered to calculate the \(C_{DC}\), presented as follows [45,46]:  
\[C_{DC,hold}\geq\frac{2P_{rat}t_{hold}}{V_{DC-link}^{2}-V_{DC-link,min}^{2}} \tag{2}\]  
and  
\[C_{DC,ripp}\geq\frac{P_{rat}}{2V_{DC-link}\pi f_{I}\Delta V_{DC-link}} \tag{3}\]  
where \(f_{I}\) is the line frequency. Among both capacitances, the maximum value is selected for the operation and mathematically obtained as follows:  
\[C_{DC}=max\big{(}C_{DC,hold},C_{DC,ripp}\big{)} \tag{4}\]
# SROR: A Secure and Reliable Opportunistic Routing for VANETs
## 5 Deep Reinforcement Learning--Opportunistic Routing  
We first discuss the problem of selecting the relay nodes in this section, and formulate this problem as an MDP. Next, the problem is solved by a DRL. The DRL model is shown in Table 2, the learning environment of DRL model is the VANETs, and the agent is each packet that needs to be transmitted. The state of the agent is the union of the node with the holding packet and its CFNs set, and this node is the one that currently holds the packet, which is abbreviated as Node-HP. The action of the agent is to select a relay node.  
The problem P1 is a sequential decision problem. To select the most suitable next-hop relay node, the problem is modeled as an MDP. Let a tuple \(\langle\mathcal{S},\mathcal{A},\mathcal{R}\rangle\) represent the MDP, where \(\mathcal{S}\), \(\mathcal{A}\), and \(\mathcal{R}\) represent the state space, action space, and immediate reward, respectively. Next, we will describe the definition of each element in detail.
## 4 Framework Components
### Internet of Things (IoT)
The SBCs on these IoT systems are powerful enough to process the data and communicate either to edge servers or directly to big data systems using multiple communication protocols. In the proposed framework context, sensors that can measure temperature, pH, turbidity, odor (E-Nose), light (infrared), taste (E-Tongue), etc., are expected to be used to measure the characteristics of food. The sensors communicate with the SBCs, edge servers, or external networks such as 4G/5G, Local Area Communication (LAN), Wi-Fi/Bluetooth, etc.
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 5 SR in Wi-Fi 6
### Mechanisms for Spatial Reuse in Wi-Fi 6
#### 5.3.4 Parameterized Spatial Reuse (PSR)  
Parameterized Spatial Reuse (PSR) is an alternative adaptive approach to support more concurrent transmissions. Each access point embeds its tolerable interference level and its transmission power within the Trigger Frames (TFs) to inform overlapping BSSs about concurrent uplink transmissions [98]. The maximum interference level depends on the RSSI, the minimum SNR of the highest MCS, and a safety margin. It is computed in a way that interfering signals with this strength at the most do not impair the reception of valid signals. Receiving stations measure the RSSI and use the transmission power coded in the frame to compute the loss in signal strength. The maximum interference level and this loss of signal strength propose a maximum value for the receiving station's transmission power. The station must apply a transmission power below the proposed values for all neighboring access points. This mechanism may lead to low transmission power, low MCS, and longer data transmission.
## 2 Experimental Section
### Data  
#### 2.2.1 Gross Primary Productivity  
Data processing and instrumentation is identical at both sites. Eddy covariance (EC) derived surface fluxes of carbon, water and energy were measured at 10 Hz using a 3-axis sonic anemometer (CSAT-3, Campbell Scientific, Logan, UT, USA) and an open-path infrared gas analyzer (Li-7500, LiCor Biosciences; Lincoln, NE, USA). Continuous measures of net radiation, air temperature and relative humidity, soil temperature, photosynthetically active radiation, and soil moisture (volumetric water content) were made using a CNR1 (Kipp and Zonen, Delft, The Netherlands), HMP45C (Vaisala RH probe with aspirated radiation shield, Vantaa, Finland), TCAV (averaging thermocouple probes, 27 per site), up facing quantum sensors (Li-190 SB, Licor Biosciences, Lincoln, NE, USA) and ECHO probes (TE 5 cm, Decagon Devices, Pullman, WA, USA, 27 per site) respectively. The fluxes were aggregated to 30 min intervals and were corrected for temperature and moisture variations (WPL, [36]) as well as frequency responses according to [PERSON] [37]. Anemometer tilt due to terrain variability was corrected using a planar fit method. We used a friction velocity (u*) filter to reject data obtained when turbulence is low (u* less than a threshold value). Data gaps created by the u* filter, malfunctioning instruments, and rain were filled following [PERSON] _et al._[38]. We partitioned the gapfilled net ecosystem exchange (NEE) into the components of total C uptake through photosynthesis (_i.e._, GPP) and total carbon leaving the ecosystem through both autotrophic and heterotrophic respiration (Re). We used exponential relationships between nighttime NEE and temperature following the methods of [PERSON] _et al._[38] to calculate continuous ecosystem respiration during the day. GPP was then calculated as NEE--Re [39].  
We used a flux source area model [40] modified for 2 dimensions by [PERSON] _et al._[41], to characterize the experimental region at both sites that are measured by the flux towers. The source area model suggested that the four hectare analysis region at each site accounted for approximately 80% recovery at the tower level. In this way we ensured that the remote sensing data and the EC measurements were representing the same patch of vegetation.
# Investigation of 2 DOF PID Controller for Physio-Therapeutic Application for Elbow Rehabilitation
## 3 Controller Design  
### 2 DOF PID Controller Design  
This study adopts a 2 DOF PID controller in order to control the flexion and extension movements of the prototype. Here, the 2 DOF PID controller receives a current of different amplitudes as control inputs and the system provides the angular displacement and, hence, the higher the magnitude that is given to the system, the higher the angular displacement that will be generated by the system. Therefore, if the system does not perform well, the system may bring harm to a patient's impairment. The 2 DOF PID controller primarily includes two controllers, where one controller, \(C_{r}(s)\), is responsible for the reference input and another controller, \(C_{y}(s)\), is responsible for system output of the system, as shown in Figure 6.  
Here, the algorithms for both \(C_{r}(s)\) and \(C_{y}(s)\) can be shown as follows in Equations (5) and (6).  
\[C_{r}=a\cdot K_{P}+\frac{K_{I}}{s}+b\cdot\frac{K_{D}}{N+1}\cdot s \tag{5}\]  
\[C_{y}=K_{P}+\frac{K_{I}}{s}+\frac{K_{D}}{N+1}\cdot s \tag{6}\]  
In its algorithm, the set-point weight is considered with the reference because it can reduce overshoot at the set-pint response and, thus, smooth the response [35].  
The final control law of the controller can be mentioned as Equation (7).  
\[u=K_{P}(a.r-y)+K_{I}\frac{1}{s}(r-y)+K_{D}\frac{N}{1+N\frac{1}{s}}(b.r-y) \tag{7}\]  
Figure 4 represents the revolution of the inertia tensor using a homogeneity transformation and the elbow joint angle in Simulink. The function of the elbow joint model is shown in the figure: the segment and corresponding arm increase gradually.
## 1 Introduction
Therefore, stable, tunable, and efficient GVD compensation is necessary for high-power laser facilities. In this paper, we propose a modified, active, closed-loop feedback compensation device for GVD-induced FM-AM conversion using a two-dimensional, electric, adjustable mirror mount and parallel grating pair to improve the long-term stability and efficiency. The experimental results showed that the influence of FM-AM is notably suppressed in long-term testing; the depth ranged from approximately 2.28% to 5.22%. Compared with manual regulation, the motor closed-loop feedback control affords higher accuracy, stability, and efficiency of adjustment. Moreover, the depth drifts slightly at the laboratory temperature, which cannot be fully compensated. The relationship between the depth and temperature was quantified via simulation calculations and was in agreement with the drift observed in the experiment.
# UV Light Application as a Mean for Disinfection Applied in the Dairy Industry
## 5 Application Areas
### Disinfection of Packaging Materials  
Microbiologically responsive bulk items, such as food, require packaging materials with low microorganism counts in particular. These materials surfaces can be treated in a variety of ways. UV light radiation treatment is a more successful and cost-effective approach than chemical and thermal treatments [24]. UV sterilizes the products and packaging materials used in food industries [75]. By using the necessary UV light doses to packaging materials such as packets, foils, cartons, films, cans, wrappings, bottles, seals, caps, and lids, the number of germs on the surfaces may be significantly decreased. Until filling or closing the lid, the containers can be exposed to UV light, or the prepared food can be subjected to UV-C light [10]. This could extend the shelf-life of the products, therefore, minimize production wastage and cost savings [73].  
The transmission of UV-C light through polyethylene terephthalate (PET) and polyvinyl chloride (PVC) is minimal whereas polypropylene (PP) and polyethylene (PE) permitted ample transmission of UV-C light. PP and PE transmitted 80.4% and 59.6% of UV-C light, respectively, at a wavelength of 254 nm. It was also found that thicker films are unsuccessful in lowering pathogen numbers in both the PP and PE film tests. The UV-C reduction in population of _E. coli_ O157:H7, _S. Typhimurium_, and _L. monocytogenes_ was significantly higher when inoculated cheese slices were packed in 0.10 or 0.13 mm thick PP or PE films compared to non-packaged treated samples or those packaged with 0.07 mm films [57]. Also, commercially packaged sliced cheese brands usually have a film thickness of less than 0.04 mm. Therefore, the discussed film type and thickness (less than 0.07 mm) can be used to create decontaminating interventions that shield not only sliced cheese products but also a variety of processed foodstuffs from microbial contamination after manufacturing.