esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
The investigated approaches have demonstrated positive influences on the sports facility from energy, economic, and environmental perspectives. This is particularly noteworthy when considering the typical spatial configuration of stadiums, which, especially in Italy, are often constructed away from other buildings. This spatial separation mitigates concerns related to shading from adjacent structures on the roof, ensuring optimal photovoltaic system performance and minimizing any potential decrease in energy production. The energy simulation of the photovoltaic system installable on the roof of large architectural complexes, as examined in this study for the Olympic Stadium in Rome, highlights its potential as a significant energy resource for the local community. The annual electrical energy production, amounting to 4.9 GWh, surpasses that of typical photovoltaic systems installed on the roofs of urban buildings.  
The total amount of annual energy from the PV system was found to be 212.9 kWh/m\({}^{2}\). Significantly, the spatial layout of stadiums, typically free from shading concerns, enhances the efficiency of the photovoltaic panels. This efficiency translates to a considerable advantage in terms of energy production when compared to installations on the roofs of densely populated urban buildings. Moreover, the energy demand for a citizen can be supplied by 76 m\({}^{2}\) of PV panels. The payback period, net present value, and profitability index indicate that the designed solar PV system is economically viable, offering an excellent return within a short timeframe (14 years). The electricity production from PV panels guarantees higher earnings, underlining the financial feasibility of such installations.  
The culmination of energy, economic, and environmental assessments sheds light on how the produced energy can be strategically reinvested for the local community, as explained before. These findings aim to provide a robust and pragmatic approach for football associations or local administrations, offering a model for moving towards a sustainable and economically feasible future.
## 4 Methodology of System Design
### Product Development
#### 4.2.1 Technologies and Algorithms Selection  
At this stage, the designers propose technologies and algorithms, which are in line with the desired functionalities and constraints stated by the stakeholders and future users at the problem formulation step. Then, in choosing technologies and algorithms, the constraints arising from the environment, like indoor/outdoor or high humidity, in which the designed system will operate, have to be considered. Furthermore, the suitable technologies and algorithms have to be pondered with respect to the price constraint, and then, after the primary elimination, only a few possible solutions would remain; therefore, the price may indicate the final decision. However, if there are no suitable solutions accomplishing the requirements, or the solutions lack some of the functionalities or constraints, then the designers have to propose and develop new solutions or adapt the existing ones.
## 2 Modeling Fault Detection and Fault Correction Processes
### The Relationship between \(m(t)\) and \(m_{c}(t)\)  
Here we use data collected from testing a software program (Data Set 1, DS-1) [28] to study the relationship between \(m(t)\) and \(m_{c}(t)\). Let \(r(t)=\frac{m_{c}(t)}{m(t)}\), the cumulative detected faults and corrected faults are shown in Figure 1 and the value of \(r(t)\) is shown in Figure 2. Apparently, at the beginning of the testing phase, lots of faults are detected and most of them are simple and easy to be removed, so the difference between the number of corrected faults and detected faults is small, then faults detected become more complicated and difficult to be removed, so the difference becomes larger, then the difference becomes less again. Thus, a concave or S-shaped function can be used to model the ratio of the number of corrected faults to the number of detected faults.  
Here we use two S-shaped functions to model \(r(t)\). From Table 1, we can see that \(r(t)=\frac{1}{1+\beta e^{-bt}}\) provides a better descriptive power.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**No.** & \(\mathbf{r(t)}\) & **MSE** & \(\mathbf{R^{2}}\) & **Adjusted \(\mathbf{R^{2}}\)** \\ \hline
1 & \(\frac{1}{1+\beta e^{-bt}}\) & 0.0022 & 0.9765 & 0.9749 \\
2 & \(1-(1+bt)e^{-bt}\) & 0.0038 & 0.9786 & 0.9756 \\ \hline \end{tabular} Note: The definitions of MSE, \(R^{2}\) and Adjusted \(R^{2}\) are shown in Section 3.1.  
\end{table}
Table 1: Comparison of two S-shaped functions as \(r(t)\) for DS-1.
## 2 Materials and Methods
### ESWT Methodology  
At our clinic, hyaluronic acid intra-articular injection therapy was performed under the diagnosis of varus knee OA, but there was no improvement in symptoms, and ESWT was indicated for patients with BML identified by MRI. The irradiation site was the medial condyle of the femur and the medial plateau of the tibia, or both, where the BML was observed and the area where the pain was felt.  
The shock wave was produced using a Duolith SD1 (Storz Medical, Tagerwilen, Switzerland). First, MRI confirmed the areas where the BML was found in the medial condyle of the femur and the medial plateau of the tibia. The patient's knee was maximally flexed, and the ultrasound probe was directed to the site where the BML was believed to be present to identify areas of cartilage thinning. The same part was considered to be the site where the BML existed, and the position of the irradiation target was determined. The operator's free hand gripped the handpiece vertically against the target and began irradiation, gradually increasing the power level to the limit of the patient's pain tolerance. The maximum output was up to 0.25 mJ/mm\({}^{2}\). Since the pain stimulus weakened and disappeared if the same area was irradiated continuously, we tried to irradiate the entire lesion in three dimensions from multiple directions while listening to the degree of pain of the examinee in detail. As a general rule, ESWT should be irradiated at 2500 shots at 4 Hz at least 3 times every 2 weeks. Afterward, if the improvement of symptoms was insufficient, irradiation was continued once every 2-4 weeks until the 3-month questionnaire.
## 4 Simulation and Results
Additionally, it exhibits the smallest interquartile range (IQR), with the outlier points closer to the mean value, outperforming the traditional key sampling method. This indicates its ability to handle consistent data distributions, ensuring a compact spread around the median of the estimated QBER results. In a practical QKD system, even in the absence of an eavesdropper (_Eve_) on the quantum channel, the observed QBER after quantum information distribution is not a consistent value. In this case, the error estimation method must achieve high accuracy to ensure reliable operations, even with varying observed error rates in each QKD cycle. Figure 5 and Table 3 present the corresponding performance of various QBER estimation methods by considering the variability of a QKD system over three observed QBER  
Figure 4: Comparison of QBER estimation methods: random key sampling with 5% and 10% sampling rates from the sifted keys and the syndrome estimation using a block length of \(N=64,800\) bits with \(R_{C}^{(max)}=9/10\) and \(N_{ab}=16,200\) bits with \(R_{C}^{(max)}=8/9\). These results are presented using box plots at four distinct observed QBER values: (**a**) 2%, (**b**) 4%, (**c**) 7%, and (**d**) 10%. For each observed QBER value, the results of all QBER estimation methods were derived from 2000 iterations. ranges--low (1.00-3.50%), middle (3.51-7.00%), and high (7.01-11.00%) error rates--averaged over 2000 iterations. The proposed syndrome-based QBER estimation with a block length of \(N=64\),800 bits consistently performs better than the random key sampling methods over the observed QBER ranges from low to high error rates. As illustrated in Figure 5 and Table 3, this approach achieves the highest mean accuracy (%) and the lowest variability of estimated QBER, as evidenced by its minimal standard deviation (SD). Moreover, its low mean squared error (MSE) further indicates a superior correspondence between the observed and estimated QBER values. While traditional random key sampling serves as a practical and straightforward method for channel error estimation, it must discard a portion of the sifted key that is disclosed for QBER estimation, depending on the sampling rate. In contrast, the syndrome-based QBER estimation method avoids discarding sample keys, thereby preserving the entire size of the sifted keys after channel error estimation for inputting to the subsequent information reconciliation step. However, the amount of syndrome information revealed during the syndrome-based estimation constitutes a portion of the information leakages. This portion is also considered a subset of the syndrome information used in the proposed rate-adaptive reconciliation. These leakages are subsequently mitigated during the privacy amplification step.
## 3 Geographical Adaptive Fidelity (GAF)  
In our protocol, sensor nodes within a cell periodically negotiate among each other to elect the coordinator in every round. For each round, only one node stays active to be a coordinator, while the others fall into sleeping mode. Doing this significantly reduces the energy consumption because nodes in the idle state spend much more energy as compared with the sleeping state. Analysis in [31] has shown that energy consumption ratio for _sleep:idle:receive:transmit_ is 0.13:0.83:1:1.4. It also reduces the network congestion because the number of nodes participating in transmission/reception is decreased. On the other hand, frequent change of coordinator role helps the particular nodes not running out of its energy quickly. Therefore, it can prolong nodes as well as the network lifetime. In order to control nodes in different states and transition, we employ _Geographical Adaptive Fidelity(GAF) protocol [15]. The GAF conserves energy by identifying nodes that are equivalent from a routing perspective and then turning off unnecessary nodes, keeping a constant level of routing_fidelity_.
# Explainable Post-Occupancy Evaluation Using a Humanoid Robot
## 3 Explainable POE Approach
### Environmental Comfort  
Although the standards define comfort, its determination represents a quite difficult task because subjective factors influence it. Indeed, it depends on the perception of the world given by our five senses, leading to an individual perception of the degree of comfort. In this work, we take into consideration three kinds of comfort [44]: thermal, visual, and acoustic comfort.  
* Thermal comfort: Thermal comfort depends on our perception of the environmental temperature. Our body performs within an internal temperature range much narrower than external temperatures. In particular, due to metabolism, heat is generated and transmitted to the external environment. High external temperatures hinder this process, and a warm sensation is perceived.  
Conversely, when external temperatures are low, a feeling of cold is experienced. Furthermore, the relative humidity plays a relevant role in providing the perception of thermal comfort. During frosty winters, high levels of relative humidity cause a more intense perception of cold. On the other hand, in hot environments, a greater sensation of warmth can be produced by high levels of humidity.
* Visual comfort: Visual comfort is related to the quantity and quality of light. Both insufficient and excessive intensity of light may cause visual discomfort. Visual comfort encompasses a variety of aspects, such as views of outdoor environments, the quality of light, as well as the lack of glare.
* Acoustic comfort: Acoustic comfort is achieved by minimizing noise. This improves concentration and allows for better communication.  
Hence, the discomfort is experienced when the thresholds of the environmental parameters are approached. On the contrary, the sensation of comfort is mainly correlated with the ranges of the acceptability of such parameters. Several standards propose such ranges. In particular, ASHRAE 55-92 [3] is a standard that provides the recommended values of environmental parameters for thermal well-being, as is shown in Table 1.  
As concerns visual comfort, the European standard EN 12464-1 [17] defines the quantity and quality of illumination as lighting requirements for indoor work spaces. It covers offices, places of public assembly, restaurants/hotels, and theaters/cinemas. Moreover, recommended light levels are provided according to the task an individual has to perform. An example is provided in Table 2. Noise Rating (NR) [45] i as standard to measure and quantify noise in buildings. Table 3 reports an excerpt of the NR levels that are recommended for different application areas. Moreover, the mapping of NR levels with the sound pressure level expressed in decibels (dB, the commonly used unit of measurement) is provided by the noise rating curves. For example, for spaces with office end-use, the maximum sound pressure level is 55 dB.
# A Miniaturized RHCP Slot Antenna for Wideband Applications Including Sub-6 GHz 5G
## 3 Design Steps
It is clear from Figure 5a,b that the parasitic patch has an enormous effect on the impedance BW and a very subtle effect on the ARBW. The reason behind this is that the parasitic patch improves the coupling between the feedline and the radiating slot without modifying the current direction or the phase angle. The location of the rectangular parasitic patch is chosen behind the rectangular tuning stub, and its shape follows the original shape of the stub. The location is also selected in such a way that it does not overlap with the ground plane to improve the coupling between the feedline and the slot. Figure 2: Design steps for the proposed wide-slot antenna. The shape of the proposed antenna is unique compared to other wide-slot antennas. The proposed design has multi-section asymmetric tuning stub and additional parasitic patches at the ground plane, which are completely different from the tuning stubs and ground structures used in [25; 35; 51]. This design enhances the circular polarization by effectively exciting the orthogonal modes. Additionally, the asymmetric wide-slot structure in the ground plane differs from typical rectangular or elliptical slots, contributing to generating a wider AR bandwidth. These structural innovations clearly distinguish the proposed antenna from prior works. Like the other wide-slot antenna designs, the tuning stub shape is carefully designed to optimize the line-slot coupling of the antenna. Meanwhile, the single-feed microstrip line for the CP antenna was chosen because it reduces the design complexity compared to dual-feed antennas. This mechanism is simple since it generates circular polarization through the slot geometry without the need for complicated external networks. Additionally, it is compatible with compact devices, such as smartphones and IoT devices, where a small size is required. The designed antenna size is suitable for the following applications:  
* 5G sub-6 GHz (3.4-3.8 GHz), which requires compact size devices such as smartphones. * IoT systems and Wi-Fi 6E (2.4-5 GHz), which also require small-sized devices with a wide range of frequencies. Figure 4: The location of the rectangular parasitic patch with respect to the tuning stub. Figure 3: Simulation results of the proposed wide-slot antenna design steps: (**a**) reflection coefficient and (**b**) AR.
# Understanding the Light Detection in an Integrated Novel Electrophotonic Wavesensor Photodetector
## 4 Electrical Behavior and Photodetection
### General Summary of the Principles of Operation  
The WS integrates a light emitter, waveguide, and a photodetector. In spite that it is a MOS-like structure, its operation is significantly different from the usual electrical amplification mechanisms. First, the proposed sensor is a bulk device, not a surface one. The metal gate has the only purpose of controlling the surface to avoid superficial current flow. The waveguide is also the dielectric under gate, making these devices electrophotonicand not electronic nor photonic. The device works in Punch-Through condition, extending as much as possible the sensing region length, increasing the photon conversion. In addition, the light is directly absorbed in the depleted region, without passing through highly doped or non-fully transparent materials. In Punch-Through condition, the high electric field moves the carriers towards the drain terminal. Drain current consists of two components: one produced by the carriers generated by light, and another made by electrons injected from Source. In the presence of light, the latter is increased due to a reduction of the barrier in the source-substrate interface, adding extra carriers to those directly photogenerated. This results in a gain on the photocurrent.
# A Study of Forest Phenology Prediction Based on GRU Models
## 3 Results and Analysis
### Model Stability Test  
The stationarity of a time series is a prerequisite for time series analysis, which is required in the law of large numbers and the central limit theorem. Many modeling processes are established under the premise of these theorems, and many conclusions obtained are unreliable if these conditions are not met. Stationarity testing can be performed using two methods: the observation method and the unit root test method. The observation method examines whether the trend and correlation graph of the sequence exhibit certain periodic factors with time. The test for stationarity was conducted using the observation method, and a statistical test module named teststationarity was edited in Python to construct an autocorrelation graph, limiting the lag values on the x-axis to 20 to more intuitively display the statistical test results (Figure 8).  
The results showed that the autocorrelation coefficients gradually approached zero in Figure 8. Among the three preset points, preset point 1 exhibited an overall tendency to approach zero, preset point 3 underwent oscillations before approaching zero, and preset point 5 exhibited small disturbances after approaching zero. All three preset points demonstrated that the data of the GRU model on the training and testing sets stabilized as the time series progressed. The time series tested by the GRU model exhibited stationarity.  
Figure 7: Data distribution plot of the GRU model.  
Figure 8: Autocorrelation plot of the GRU model.
# Comparative Analysis of Audio Features for Unsupervised Speaker Change Detection
## 3 Methodology  
In order to explore the effectiveness of various audio features and different distance metrics for the SCD. We use unsupervised metric-based approach, which computes the distance between two consecutive audio features. If the distance value is larger than a predefined threshold, then it is considered as speaker change point.  
Figure 1 describes the overall framework of the study. The flowchart outlines the methodology for SCD, starting with audio data collection, preprocessing (including pre-segmentation and voice activity detection), and feature extraction (e.g., MFCC, mel-spectrogram). Following this, one method is chosen: either BIC-GMM, which evaluates performance directly on the test set, or KL-GMM, which involves threshold optimization on the validation set before evaluation. The process concludes with the results stage, where metrics like miss Detection rate (MDR) and false alarm rate (FAR) are calculated, summarizing the study's key findings and results.  
Figure 1: An overall framework of the study.
## 1 Introduction
Some methods employ multi-supervision architectures, but these merely decompose tasks into layers, with each module supervised by its own loss function and ground truth, as shown in Figure 2b. In such training processes, there is no information interaction between modules. Therefore, it is necessary to improve the integration of different components to handle complex tasks like weak small ship detection under thin cloud cover. Deep network models operating under a single supervision paradigm face challenges when dealing with such complex tasks. Establishing a hierarchical supervision learning paradigm is crucial for overcoming the difficulties of using deep networks to model complex tasks. Inspired by the challenges identified in previous studies, this research proposes the Cloud Removal and Target Detection (CRTD) network, designed to address the detection of small ship targets obscured by thin cloud cover in optical satellite imagery. The CRTD network integrates a cloud removal module (TCR) and a small target detection module (STD) within a dual-stage hierarchical supervision framework. The goal of this study is to enhance the detection accuracy of small, weak ship targets in remote sensing images affected by thin clouds by improving preprocessing techniques, refining feature extraction, and ensuring a seamless integration between cloud removal and target detection stages. Specifically, this work aims to minimize detection errors, such as false positives and missed detections, in challenging environments where cloud interference is prevalent. The primary contributions of this article are as follows: (1) the development of the CRTD network, which effectively combines TCR and STD to address the detection of small ship targets under thin cloud cover; (2) the introduction of a unified Region of Interest (RoI) [9] loss function, facilitating end-to-end training and improving feature alignment between the cloud removal and detection tasks; and (3) the creation of a custom GaoFen-4 satellite dataset, enriched with thin cloud scenarios, which provides a robust benchmark for evaluating small ship detection algorithms. Extensive experiments conducted on this dataset demonstrate the superior performance of CRTD in detecting small targets under conditions of thin cloud cover. The remainder of this article is structured as follows: Section 2 provides a review of related work on cloud removal and small target detection in remote sensing imagery. Section 3 presents the CRTD framework, including a detailed description of TCR, STD, and the unified RoI-Loss function. Section 4 presents the experimental results, followed by a discussion comparing CRTD with state-of-the-art methods and highlighting its advantages. Finally, Section 5 concludes the paper with a summary of the findings and outlines potential directions for future research.
## 4 Simulation Scenario with Ultra-Dense Urban Environment
### Performance Evaluation
As depicted in Figure 11, the environment without the DDLB algorithm shows an attach failure rate of 5.62%. The DDLB algorithm reduced this rate significantly, with the 1-min execution interval achieving an improvement of 8.54% and the 3-min interval achieving an improvement of 27.94%. Figure 9: Handover ping\(-\)pong ratio during all simulations. Figure 11: Attach failure ratio during all simulations. Figure 10: System overloaded state ratio during all simulations. Furthermore, the environment without the DDLB algorithm achieved a relatively low mean value of RLF of 1.24%. Interestingly, the DDLB algorithm with both execution intervals of 1 and 3 min maintained these low mean values of RLF with slight variations of 1.20% and 1.10%, respectively, as shown in \[Attach\ Failure(\%)=\frac{Failed_{attachAttemps}}{Total_{attachAttemps}} \times 100\]0