esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
#### Strengths and Limitations of the Systematic Review  
The current literature review provides the reader with a detailed overview of the most recent VR applications aiming to reduce stress in healthy adult populations and help them develop adequate coping resources. The benefits of using VR systems for stress management can inspire scientists to develop and carry out evidenced-based programs for populations, for instance, with poor access to mental health services. However, there are a number of limitations of the present review that should be considered. Although the MINORS for the quality assessment of the sample was an asset for the review, the diversity of the interventions and instruments used for stress hampered the comparison of study findings, data-pooling, and meta-analysis. However, future authors are highly encouraged to apply such assessment tools, as they help with the methodological evaluation and critical appraisal of the included studies. Another limitation was that this review focused only on stress reduction, ignoring perhaps significant effects of VR on other outcomes, such as self-efficacy and mindfulness. As mentioned above, the study sample employed VR in different ways to fulfill diverse research aims, varying from testing nature environments to comparing VR interventions with non-technology-supported programs. Hence, if the authors had also analyzed factors directly related to relaxation, this might have partially distracted the reader from the main aim of the review. Following effects that are interwoven in stress management and in relaxation methods should be examined carefully in future research works. Moreover, the area of VR systems for stress management and prevention is growing rapidly. This may lead to the study of this field becoming quite challenging by demanding the use of reasonably strict search terms and protocols [81]. Thus, it is possible that articles have been unknowingly missed, despite multiple databases being screened. In line with this, another limitation was that the literature search was restricted to articles published in English only.
# Operational Optimisation of a Non-Recuperative 1-kWe Organic Rankine Cycle (ORC) Engine Prototype
## 2 Experimental Methods
### Time-Resolved Data Acquisition  
The ORC test rig is connected to a data acquisition system (namely the DAQ970A model by Keysight Technologies UK Ltd., Wokingham, United Kingdom) and relevant power supplies for data collection. A total of 21 pressure transducers (model PXM309 from Omega Engineering, Manchester, United Kingdom) and 21 thermocouples (T-type) are installed at various positions, mainly at the inlet and outlet of each component in the rig, of which 17 sets of sensors (one pressure transducer and one thermocouple) are used to monitor the state parameters of the working fluid, while the remaining 4 sets are used to record the temperature and pressure of the heat source and the heat sink. Three flow meters--a high-precision Coriolis flowmeter (model Optimass 6000 by Krohne Ltd., Northamptonshire, United Kingdom), a turbine flowmeter and a paddlewheel flowmeter--are used to measure the working-fluid, the heat-source (oil) and the cooling-water mass flowrates, respectively. A power analyser (model HMC 8015 by Rohde & Schwarz UK Ltd., Fleet, United Kingdom) is  
Figure 1: Piping and instrumentation (P&I) diagram of the organic Rankine cycle (ORC) apparatus.  
connected to the generator to measure the voltage, currency, frequency and the consumed electricity by the resistance. The gross power output of the ORC system, that is, the power generated by the scroll expander, is obtained. The power consumed by the pump is measured by a power meter (model PM 231E by Hugo Brennenstuhl Gmbh & Co., Tubingen, Germany), which is subtracted from the gross power output to determine the ORC net power output.  
All the aforementioned sensors/measurements are connected to the data acquisition (DAQ) system, which allows quick data acquisition. In addition, the changes of the parameters such as pressure, temperature and other thermodynamic or transport properties can be captured and used for detailed analysis and dynamic investigation of component and system performance, which is of particular importance under transient conditions. The DAQ has a scan speed of 450 channels per second. A steady-state measurement entails data
# Evaluation of Conifer Wood Biochar as Growing Media Component for Citrus Nursery
###### Abstract  
(1) Background: The commercial sustainability of the citrus nursery industry involves cutting costs by using alternative planting substrates to replace (or partially replace) the conventional black peat. Conifer wood biochar was evaluated as a component of the growth medium in a commercial citrus nursery for Carrizo citrange seedlings. (2) Methods: Seven growth media mixtures (A-G) were tested. Each mixture consisted of 50% sandy volcanic soil with the remaining 50% made up as follows: A = black peat and perlite 1:1; B = biochar 1; C = black peat, perlite, and biochar 0.5:1:0.5; D = black peat and biochar 1:1; E = black peat, compost, and biochar 0.5:0.5:1; F = black peat, perlite, compost, and biochar 0.5:0.5:0.5:0.5; G = black peat and lapillus 1:1, this substrate, previously adopted by the hosting nursery, was the control. (3) Results: The best media for the rootstock studied here were those containing 25% biochar (mixtures D and E). In the deeper layers the substrate was more compact, and the roots were limited to the shallow layers of the pot. (4) Conclusions: Conifer wood biochar can be partly added in place of peat in growth media mixtures, thus reducing costs and ameliorating sustainability.  
black peat; Carrizo citrange; charcoal; nursery costs; sustainability +
Footnote †: journal: Applied  
Article
# REISCH: Incorporating Lightweight and Reliable Algorithms into Healthcare Applications of WSNs
## 3 Preliminary Techniques for Our Data Collection Scheme
### Threat Model  
Building a threat model in HWSN is important to identify serious attacks on patients' data and subsequent disclosure. HWSN provides important services to the health sector compared to traditional computer networks, such as LAN and MAN, but they are more vulnerable than the latter. These networks rely on self-organization and synchronization to increase the flexible communications of sensor nodes, but HWSN suffers from a security vulnerability. Because of the wireless radio signals in WSN, it is easier for attackers to intercept data transmitted among sensors nodes, \(CH\)s, and \(LS\). These networks are targeted for many attacks that exploit resource-constrained, untrustworthy communication and unattended processes. HWSN threats are as follows:* The attacker performs a man in the middle (MITM) attack to modify or replay attack to resend the data to the \(CH/LS\). The attacker's aim is to use his/her device as a legitimate sensor in the network.
* The attacker can execute a denial of service (DoS) attack on the \(CH/LS\). This attack exploits a heavy transmission of duplicate or counterfeit data to destroy the HWSN.
* The attacker can apply several types of localization attacks such as Sybil, Wormhole, and Sinkhole to intercept of network communications.
* The attacker performs an attack to penetrate the EMR repository in the \(LS\), to access the patient's data and reveal their identities.
* The attacker can launch an eavesdropping attack to obtain patients' data, and then perform an analysis of these data to detect the linkability among data, information, and pseudonyms.
* The attacker can copy a legitimate sensor ID in more than one counterfeit sensor. These counterfeit nodes send modified data to the network (node replication attack).
* Collision, preimage, and second preimage attacks can be implemented to change signatures and data transferred between a network's devices.
## 3 Simulation-Based System Architecture
### Public Transport Journey Planner  
The PT Journey Planner is a schedule-based route planner that computes the PT routes. The procedure is to pre-compute them using rRAPTOR (an extension of RAPTOR in [13]), which computes bi-criteria range queries containing all Pareto sets of journeys for all departures within a time range considering two criteria: the arrival time and the number of transfers. For our intermodal scenario, the maximum number of segments, \(K\), in a PT route will be \(K=2\). i.e., one internal transfer at most in the PT leg. This is because the combination with RP adds more transfers, and this would probably make the intermodal system less appealing and comfortable. This procedure calls RAPTOR multiple times for every origin stop and departure, sorting every departure in decreasing order so that previous calculations can be reused as an upper-bound. Furthermore, for the requirements of this work, the standard version of RAPTOR has been modified in two ways. First, the target pruning is removed so that each RAPTOR execution computes all the PT routes from one origin stop to any other stop (for every single departure). Second, this algorithm permits the last segments to be of walking mode, which is not of interest in the intermodal scenario because the stops are used as the access nodes for transferring between RP and PT. Thus, the \(K\) rounds are divided into \(2K\) rounds so it keeps every route and transfer segment individually. Then, only the resulting PT routes ending with a route segment are valid.  
When the PT journey planner receives a request for a trip between a given pair of stops, it searches for the earliest one in the pre-computed data which departs right after the specified time. This approach minimises the most time-consuming part of the PT leg calculation because they are already pre-calculated in advance. However, total memory requirements are increased, since all paths comprised within a wide time window must be stored.
# The Effect of Data Transformation on Singular Spectrum Analysis for Forecasting
## 2 SSA Forecasting
### Vector Forecasting  
In contrast, the SSA Vector forecasting algorithm has proven to be more robust than the _R-forecasting_ algorithm in most cases [25]. Let us define \(\mathbf{U}_{k}^{\
abla}=\begin{bmatrix}\mathbf{u}_{1}^{\
abla}&\dots&\mathbf{u}_{k}^{ \
abla}\end{bmatrix}\) as the \((m-1)\times k\) matrix consisting of the first \(m-1\) elements of \(k\) eigenvectors. The vector forecasting algorithm computes\(m-\)lagged vectors \(\hat{\mathbf{z}}_{i}\) and constructs a trajectory matrix \(\mathbf{Z}=\begin{bmatrix}\hat{\mathbf{z}}_{1}&\dots&\hat{\mathbf{z}}_{n}&\hat{\mathbf{z}}_{n+1}& \dots&\hat{\mathbf{z}}_{n+h}\end{bmatrix}\) such that  
\[\hat{\mathbf{z}}_{i}=\left\{\begin{array}{ll}\mathbf{s}_{i}&\text{for }i=1,2,\dots,n;\\ \left(\begin{pmatrix}\mathbf{U}_{k}^{\
abla}\mathbf{U}_{k}^{\
abla^{\prime}}+(1-\sum_ {j=1}^{k}u_{mj}^{2})\mathbf{a}\mathbf{a}^{\prime}\end{pmatrix}\hat{\mathbf{z}}_{i-1}^{ \bigtriangleup}\right)&\text{for }i=n+1,\dots,n+h.\end{array}\right. \tag{7}\]  
where \(\mathbf{s}_{i}\) is the \(i\)th column of the reconstructed signal matrix \(\mathbf{S}_{k}=\sum_{j=1}^{k}\sqrt{\lambda_{i}}\mathbf{\mu}_{j}\mathbf{v}_{j}^{\prime}\), and \(\hat{\mathbf{z}}_{i}^{\bigtriangleup}\) is the last \(m-1\) elements of the vector \(\hat{\mathbf{z}}_{i}\).  
After a diagonal averaging of the matrix \(\mathbf{Z}=\begin{bmatrix}\hat{\mathbf{z}}_{1}&\dots&\hat{\mathbf{z}}_{n}&\hat{\mathbf{z}}_{n+ 1}&\dots&\hat{\mathbf{z}}_{n+h}\end{bmatrix}\) constructed by employing Equation (7), we obtain a time series \(\{\hat{z}_{1},\dots,\hat{z}_{N},\hat{z}_{N+1},\dots,\hat{z}_{N+h}\}\), as has also been explained in [28] (Section 2.3). Thus, \(\hat{x}_{N+j}=\hat{z}_{N+j}\) produces a forecast corresponding to \(x_{N+j}\) for \(j=1,\dots,h\).
## 2 Literature Review
### Research Hypotheses  
According to the previous theoretical analysis, the influence of views on the serial position effect inducing the user's decision-making bias mainly occurs in the memory stage. The memory formed by a user in SE is a series of independent impressions; it is minimally affected by the context and transforms into long-term memory with the continuous refreshing and accumulation of subsequent information [38]. In this environment, when the user reconstructs a memory reading before the final decision, as the end target stimulus remains in the working memory, it is more likely to have a recency effect than the intermediate stimulus [39; 40; 41]. In JE, the memory formed by the user is comparative. The user does not refresh the interface view in the continuously presented information group. Therefore, the user's memory of the target stimulus is relatively integrated. Compared with a stimulus that appears later in the same sequence, the stimulus presented in the first paragraph has undergone a more extensive rehearsal, which leads to better consolidation and long-term tracking comparison. It is more likely that there will be a primacy effect [42; 43]. Of the factors that constitute the evaluation mode, information presentation is the most important factor. In theory, the quantity of information and collapse will also change the degree of influence of the context. They can each be used as a variable for experimental comparison to observe their effects and be used as a noise factor to eliminate the influence on information presentation. Based on the aforementioned review, we propose three hypotheses that corresponding to the research questions, respectively (The experimental relationship corresponding to the research question is shown in Figure 2).  
**Hypothesis 1 (H1).**_The serial position effect in the online shopping environment will induce user decision-making bias, and the user's evaluation mode and their preference will be reversed as the view type changes._  
**Hypothesis 2 (H2).**_The change of views in information visualization affect the decision-making bias in human-computer interaction induced by the serial position effect. User decision-making results under SE will be more prone to the recency effect, while user decision-making results under the JE mode are more prone to the primacy effect._  
**Hypothesis 3 (H3).**_The three factors that constituted different evaluation modes: information presentation, the quantity of information, and folding of details, play important roles in preference reversal._  
Figure 2: The relationship between the experimental groups in Experiment 1 and Experiment 2.
## 4 Results and Discussion
### Performance Comparison Analysis  
The following will be a comparative analysis of HDS, DWDS and HDS without a separated heat pipe to better reflect the superiority of HDS performance and the beneficial effects of a separated heat pipe on performance. Figure 7 shows the schematic of DWDS and HDS without a separated heat pipe.  
Figure 8 shows \(D_{c}\) and \(SMER\) of three dehumidification systems under in inlet air cases. The operating parameters of the three systems are consistent. Process air volume is 400 m\({}^{3}\) h\({}^{-1}\), the regeneration air volume is 133 m\({}^{3}\) h\({}^{-1}\) and regeneration temperature is 100 \({}^{\circ}\)C. It can be seen from Figure 8a,c that the \(D_{c}\) of HDS is significantly higher than that of DWDS and the ratio of \(D_{c}\) rises up with the increment of inlet air temperature and moisture content. This shows that HDS has better case adaptability than DWDS in high-temperature and -humidity cases of s suspension bridge. It can be seen from Figure 8b,d that, in different air inlet cases, the \(SMER\) of HDS is always the highest, followed by the HDS without heat pipe and, finally, DWDS, which reflects the excellent energy conservation of HDS.  
Figure 5: Optimal cooling capacity in different cases.  
Figure 6: The effects of regeneration temperature on performance. (**a**) \(SMER\). (**b**) \(D_{c}\) and \(W_{tot}\).  
The cooling capacity required for the aftercooler directly reflects the energy consumption of the aftercooler. As shown in Figure 9, the aftercooler energy consumption of HDS is lower than that of HDS without s separated heat pipe. The \(SMER\) of HDS is higher than that of HDS without a separated heat pipe, as shown in Figure 8b,d. Thus, separated heat pipes have a positive effect on reducing the energy consumption of HDS and improving \(SMER\). On the one hand, a separated heat pipe recovers the high-temperature heat of the exhaust at the evaporator section, which reduces the energy consumption of the aftercooler. On the other hand, the regenerative air is preheated at the condenser section, reducing the energy consumption of the heater.  
Figure 8: \(D_{c}\) and \(SMER\) of three dehumidification systems. (**a**) \(D_{c}\). (**b**) \(SMER\). (**c**) \(D_{c}\). (**d**) \(SMER\).  
Figure 7: Desiccant wheel dehumidification system (DWDS) and HDS without a separated heat pipe. (**a**) DWDS. (**b**) HDS without a separated heat pipe.  
In summary, HDS facilitates the utilization of waste energy and enhances the system's efficiency, which contributes to its application in the field of suspension bridge main cable protection.
# Shadow Hunter: Low-Illumination Object-Detection Algorithm
## 2 Related Work  
### Low-Illumination Image Enhancement  
Low-illumination images commonly suffer from inadequate contrast and brightness, making it challenging for the human eyes to discern image details. Numerous low-illumination image-enhancement algorithms have been proposed, with histogram equalization [9] being a classic method. While histogram equalization is simple and computationally efficient, it can result in the loss of crucial image details due to excessive merging of grayscale levels. Building upon the dehazing technique, the enhancement algorithm [10] achieves a normal illuminated image by inversely processing the low-illumination image after dehazing. However, this approach tends to sacrifice numerous details in over-bright areas. Another alternative is a weight-sharing illumination learning enhancement algorithm with a self-calibration module [11], but it requires pre-training of an SCI network, and substantial data and computing resources. Inspired by the Retinex theory [12], which posits that object brightness is a combination of the object's own reflection component and the ambient light, several algorithms have been developed, including RetinexNet [13], MSR [14], and LIME [15].  
The significant advancements in deep learning techniques have greatly contributed to the progress of low-level image restoration algorithms, such as image deblurring and dehazing. These algorithms utilize the potential of deep learning models to efficiently restore images by reducing the effects of blur and haze. Given the difficulty of obtaining ground truth data for enhancing low-illumination images, most existing methods rely on training with synthetic data. Since the Retinex theory is more suitable for human visual characteristics, it has become popular to combine it with deep learning, such as LightenNet [16]. Zero-DCE [17] uses a zero-reference loss function to guide the low-illumination image to transform into a normal image without the need for paired data, which has a strong generalization ability. EnlightenGAN [18] is the first to successfully introduce non-paired training into low-illumination image enhancement and introduces an attention mechanism to adaptively adjust the brightness and contrast of the image.
## 3 System Architecture
### Uplink Data Path  
The uplink (UL) data goes from a user to RRUs wirelessly and then from RRUs to the DU via the optical links as depicted in the lower half of Figure 4. The transmitted data is loaded to the user via Ethernet. The FPGA streams the data to the FMCOMMS1-EBZ which converts the data to RF analog signals for transmission.  
At an RRU, the AAU amplifies, down-converts, and samples the RF signals received by its antennas. The baseband signals, sampled at 92.16 MSps by the ADCs, are subsequently up-sampled and sigma-delta modulated at 3.6864 Gbps by the FPGA. Every two sigma-delta modulated I-Q pairs and one control sequence are time-interleaved and transmitted to the DU over one fiber. The DU QSFP converts the NRZ signals back to the electrical domain. The DU FPGA de-interleaves the electrical signals, filters out the quantization noise, and down-samples the signals to 81.92 MSps. The PC collects the data via the PCIe interface. Similar to the DL, the transparency of the data path guarantees that the UL data is received simultaneously by all antennas at RRUs and streamed directly to the DU.  
A recently published work [14] proposes an SDoF-based system with all-digital pulse-width-modulation-based UL paths, whose RRU complexity is significantly lower. However, the UL signal quality degradation with respect to the DL is not negligible. The degradation may limit the possibility to exploit channel reciprocity [3].
## 4 Empirical Case Study
### Recommendation from Optimization Results  
With a budget of $750, the system recommends a configuration that costs $750, resulting in an overall customer satisfaction of 0.10072. The rank of fulfillment level is the same as the rank of importance weight. It suggests that the optimization model is at least reasonable to a certain extent. After solving the optimization model by the GRG nonlinear algorithm, the optimal product configuration is obtained as shown in Table 10, with the fulfillment level and customer satisfaction level shown in Table 14, and the recommended technical parameters shown in Table 15. The recommendation also provides an upgraded solution for the customer. The sensitivity analysis presents the nearest downgraded and upgraded configurations, as shown in Table 16. The nearest downgraded configuration has an effect on SSD. It shows that SSD is the least value-added EC for the current configuration. Therefore, it should be the first to be dropped if Company B wants to reduce the budget. This corresponds with the fact that storage has the smallest weight in overall customer satisfaction. The nearest upgraded configuration affects CPU by downgrading all other ECs, showing that CPU is the most value-added EC for the current configuration. Therefore, it should be the first to be enhanced if Company B is willing to provide a larger budget. This agrees with the fact that business performance, which greatly relies on CPU, has the highest weight in overall customer satisfaction.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**CR** & **Fulfilment Level, \(F_{i}\)** & **Customer Satisfaction** & **Importance Weight** \\ \hline Business Performance & 0.775 & 0.13561 & 0.5 \\ Multimedia Performance & 0.585 & 0.19121 & 0.15 \\ Computation & 0.602 & 0.09524 & 0.25 \\ Performance & 0.25 & \(-0.19587\) & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Optimal fulfilment level (F) and customer satisfaction (CS).