esa-sceva/satcom-chunk-collection huggingface dataset:
# Measurement Model of Full-Width Roughness Considering Longitudinal Profile Weighting
## 5 Experimental Verification and Analysis  
### Experimental Site and Devices  
The experimental site selected for this study is located within the Ministry of Transport's Highway Traffic Test Field in Majuqiao Town, Tongzhou District, Beijing. The chosen test road is a long, straight track specifically designed for experimentation, featuring a two-way, two-lane layout with each lane measuring 3.75 m in width and an effective measurement length exceeding 700 m. The specific experimental roadway within the closed area ensures the continuity and consistency of the tests, while also providing ample space to simulate various traffic environments.  
To alter the roughness of the test road, multiple sets of standard test blocks with known dimensions but varying quantities were carefully arranged and laid at different locations along the road during the experiment, as shown in Figure 5 and Table 3. The design and placement of these standard test blocks were aimed at systematically varying the road roughness, thereby simulating different levels of road conditions within specific length ranges. This ensured that they could represent a wide range of road roughness scenarios, from smooth to rough.  
During the data collection phase, a domestically produced vehicle-mounted three-dimensional point cloud measurement device was utilized. This equipment offered high-precision measurement capabilities, with both horizontal and vertical resolutions reaching 1 mm, enabling extremely fine measurements of road surface elevations. In the experimental process, the device conducted comprehensive elevation data collection along the right lane from south to north at speeds exceeding 50 km/h. Subsequently, the elevation point cloud data were output at intervals of 100 mm in both the horizontal and vertical directions. To enhance the statistical reliability of the data and minimize potential random errors, the entire measurement process was repeated 9 times. Additionally, a vehicle-mounted laser  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Types** & **Position Range (m)** & **Dimensions (mm)** \\ \hline
1 & 50–150 & 500 \(\times\) 250 \(\times\) 30 \\
2 & 400–500 & 500 \(\times\) 500 \(\times\) 5 \\
3 & 200–400, 500–600 & 500 \(\times\) 250 \(\times\) 20 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Dimensions and positions in road lanes of different test blocks.  
profiler was employed to measure the elevation at the wheel track trajectory zone of the road in accordance with JJG (Transportation) 075-2010 standards [30]. During road surface elevation collection, both vehicles traveled along the centerline of the lane.
# A Case Study on Integrating an Eco-Design Tool into the Construction Decision-Making Process
## 2 Methodology
### Case Study  
To test the eco-design tool, a partial rehabilitation of a building located at the University of Aveiro Campus, the Central Technical Area (CTA), was considered for this case study (Figure 1a). The CTA building was built in 1987 and basically consists of a reinforced concrete structure with masonry walls [37]. The selected case study considered the replacement of the existing window frames (263 m\({}^{2}\), Figure 1b) and examined three competitive alternative solutions: PVC, aluminum, and wood window frames. All windows considered in the project are located at ground level; therefore, specialized assembly methods and equipment were not considered in the analysis. In addition, to ensure an unbiased assessment of environmental and economic performance, competitive solutions with comparable U-values were selected: 1.30 W/(m\({}^{2}\cdot\)K) for wood and PVC window frames; 1.80 W/(m\({}^{2}\cdot\)K) for aluminum window frames. An exhaustive survey was conducted to identify aluminum frames with a U-value closer to 1.30 W/(m\({}^{2}\cdot\)K); however, this was not feasible.  
For each window frame material, three EPDs from the INIES platform (France) were selected as potential suppliers, and their impact indicators were examined using the developed eco-design tool. The best-performing solution for each window frame material was considered from the comparison of the alternative solutions.  
Connectivity with Building Information Modeling (BIM)  
The integration with BIM projects was designed by exporting the environmental data generated by the UAveiroGreenBuilding eco-design tool in \"*.xls\" format. Dynamo, a visual BIM programming application, was employed to create an ad hoc routine able to transfer extracted information for BIM of the construction solutions identified as optimal by the eco-design tool.  
Figure 1: University of Aveiro Campus. (**a**) The red line highlights the Central Technical Area and (**b**) the plant of the Central Technical Area.
# On Construction of a Campus Outdoor Air and Water Quality Monitoring System Using LoRaWAN
## 3 System Design and Implementation
### Sensor Deployment  
#### 3.5.1 Air Quality Sensor and LoRa Terminal Deployment  
The air quality sensors and LoRa, including the RX64M controller PCB, humidity (PMS5003T) and temperature sensors, solar panel, charge and discharge control board, LoRa module, and Gateway, are shown in Figure 5.  
Figures 6 and 7 show the gateway location and LoRa test installation of the deployment station at the campus, respectively.
###### Abstract  
Noise in shopping centers and restaurants can reduce intelligibility and affect the comfort of the employees working for long periods in such environments. In this study, the interior acoustics of a shopping mall are studied by measuring the reverberation time (RT), with no occupation, and the equivalent sound pressure levels (\(\mathrm{L_{Aeq}}\)), with occupations at different times of the day, over a period of one week. The measurements were done at different locations, chosen based on their usage (shopping areas, entrances, food courts, and playing area). The values are correlated to the subjective acoustic comfort and the experience of loudness of the staff working at these locations, as measured through a questionnaire. The average RT values that were measured ranged between 0.9 s in the food court and 1.4 s for the main entrance. The \(\mathrm{L_{Aeq}}\) was measured with a minimum of 58 dB in the shopping area in the early morning and a maximum of 83 dB in the playing area in the evening. The results showed an increase in the \(\mathrm{L_{Aeq}}\) for the afternoon and evening in comparison with the early morning and midday time (\(p<0.05\)); similarly, the sound levels for the weekends were higher than the weekdays (\(p<0.05\)). The questionnaire results showed that headaches and difficulties in communicating with others (speech intelligibility) are the most important problems induced by the noise in shopping malls, occurring for 47.1% and 38.2% of respondents, respectively.  
acoustic comfort; equivalent sound pressure levels (\(\mathrm{L_{Aeq}}\)); shopping malls; reverberation time; background noise +
Footnote †: journal: Applied Soft
# Potential of Sub-GHz Wireless for Future IoT Wearables and Design of Compact 915 MHz Antenna
## 2 Comparison of Wireless Performance in the 915 MHz and 2.45 GHz ISM Bands
### Wireless Link Characteristics
An important assumption in the following analysis is that the gain of the receiver antenna \(G_{RX}\) is specified as 0 dBi at both 915 MHz and 2.45  GHz as this represents the gain of an antenna with omnidirectional radiation characteristics that is typically  
Figure 2: IoT sensor (Tx) placed on the human wrist communicating with a remote receiver device (Rx). used in a receiver hub. However, if more directivity is required, the value of \(G_{RX}\) can be increased depending on the application. It has been also assumed that the transmitted power \(P_{TX}\) is 0 dBm. \[P_{RX}(\text{dB})=\ 10\ \log_{10}\left(P_{TX}G_{TX}G_{RX}\left(\frac{\lambda_{0}}{4 \pi d}\right)^{2}\right) \tag{3}\]  
From this calculation, the power received when the system operates at a frequency of 915 MHz, \(P_{RX\_915 MHz}\) is equal to \(-41.1\) dBm, when the system instead operates at 2.45  GHz the received power \(P_{RX\_245 MHz}\) is \(-44.3\) dBm. In light of these results, it can be concluded that also considering a limitation on the antenna form factor, an improved link budget performance can be obtained operating in the 915 MHz ISM frequency band. Table 1 summarizes the calculated results for the scenario depicted in Figure 2. The parameters and units are displayed in the first two columns. The calculated results are summarized in the third and fourth columns with the calculated term differences between 915 MHz and 2.45  GHz ISM band results denoted \(\Delta\) (dB) displayed in the right-most column. In conclusion, a key advantage of 915 MHz operation is the greatly reduced free-space path-loss (FSPL), which is 8.6 dB lower than the case at 2.45  GHz. Taking into account also the limitation on the form factor of the wrist-worn wireless SpO2 sensor, even though the theoretical achievable antenna gain \(G_{TX}\) is limited to approximately \(+0.1\) dBi at 915 MHz when compared to \(+5.5\) dBi at 2.45  GHz, there is a net increase in received power of approximately 3.2 dB assuming that \(G_{\text{RX}}\) is limited to 0 dBi. However, if it is possible to increase the value of \(G_{\text{RX}}\) by increasing the directivity of the receiving antenna or by employing additional Rx antennas, then the value of \(G_{\text{RX}}\) could be doubled (3 dB) by employing two receive antennas for example. In this case, the calculated results in Table 1 show that the received power at 915 MHz and 2.45  GHz is approximately the same or a 0.16 dB difference.
# Fast Reconfigurable SOA-Based Wavelength Conversion of Advanced Modulation Format Data +
## 6 Conclusions  
The wavelength conversion of data with advanced modulation formats will play a significant role in next generation optical networks. FWM is one of the most advantageous technologies for the wavelength conversion of data employing advanced modulation formats. We theoretically study the phase noise transfer issue for a nondegenerate FWM scheme and the vector theory in nonlinear SOAs. According to these phase noise and polarization studies, we design the efficient and impairment-free SOA-based wavelength converter based on FWM. We demonstrate a rapidly reconfigurable SOA-based FWM wavelength conversion system using a fast-switching, tunable SGDBR laser as one of the pumps and experimentally study the wavelength conversion of QPSK and Pol-Mul QPSK signals at 12.5-Gbaud, with total data rates of 25 Gbps and 50 Gbps, respectively, using the proposed scheme. A wide tuning range (>10 nm) and a fast wavelength conversion time under 50 ns and 160 ns have been achieved for the proposed reconfigurable wavelength conversion system for QPSK and PM-QPSK signals, respectively. The reconfiguration time is mainly affected by the combination of the switching time of the tunable pump laser and the CMA convergence time in DSP. The performance under the switching environment after the required reconfiguration time is the same as the static case when the wavelengths are fixed, which makes it feasible to develop fast reconfigurable wavelength converters for dynamic, adaptive, and bandwidth-efficient optical networks by using rapid switching tunable pump lasers in conjunction with fast tuning optical filters.  
This work has been supported by Science Foundation Ireland through grant numbers 13/RC/2077, 12/RC/2276, 15/US-C2C/I3132, and the HEA PRTLI 4 INSPIRE Programmes.  
[PERSON] conceived the initial ideas; [PERSON], [PERSON], and [PERSON] designed and performed the experiments; [PERSON], [PERSON], and [PERSON] wrote the paper. [PERSON] and [PERSON] reviewed the results and the manuscript.  
The authors declare no conflict of interest.
# Energy Communities Design Optimization in the Italian Framework
## 2 Materials and Methods
### Benefit Distribution  
The collective self-consumption can be modeled as a cooperative game [15], where the players are the condominium itself and the set of private users located in the building. The economic value generated hour by hour depends on the presence and the interaction between load and production; in particular, on the quantities of energy injected in the network and shared. The revenue obtained by the configuration can be represented with the following value function:  
\[v(S)=E_{injected}\cdot EP+E_{shared}\cdot Inc \tag{7}\]  
where \(S\) is the coalition of players that join the configuration, \(EP\) is the value of the energy injected into the distribution network and is equal to the market price, \(Inc\) is the value of the incentive according to the Italian regulation (see Table 1). Both the condominium and the single consumers want to take advantage of the participation in the collective configuration. The condominium invests in the installation of new generators and equipment and wants to obtain a return from the investment. On the other hand, when considering consumers, they have a certain contractual power, as they are necessary to generate the profit (mainly based on the incentive for shared energy). As a consequence, they can require an appropriate return for their participation in the collective self-consumption configuration. The objective of studying the game is to find a stable and fair allocation rule that gives an adequate payoff to the players, so that each one is encouraged to take part in the EC. The adopted solution is based on the Shapley value, a well-known solution that reflects the fairest payoff for the players, taking into account the marginal contribution of each one. Indeed, value should be distributed among consumers according to their contribution to value generation (not everyone contributes in the same way). The contribution of a passive user will be zero if their energy demand occurs at a time when the total load already exceeds the current production. On the contrary, a user will produce a greater value if they consume the energy produced by the facilities of the community when there are no other users who require it. The Shapley value takes into account this effect. For user \(i\) it is evaluated as:  
\[\Phi_{i}(v)=\sum_{S\subseteq N\setminus\{i\}}\frac{|S|!(n-|S|-1)!}{n!}(v(S \cup\ \{i\})-v(S)) \tag{8}\]  
where the marginal contribution \((v(S\cup\ \{i\})-v(S))\) of the player \(i\) in the coalition \(S\) is weighted on the factor \(\frac{|S|!(n-|S|-1)!}{n!}\) that considers the possible orders in which player \(i\) can join the coalition \(S\). The distribution rule defines a payoff for each member of the community.
## 2 Experimental Setup  
### Experimental Specimens and Data Acquisition System  
All of the reinforced concrete (RC) beam specimens are identical in terms of specifications: 240 \(\times\) 15 \(\times\) 30 cm in length \(\times\) width \(\times\) height; compressive strength 24 MPa; and five reinforcing bars of 16-mm diameter (Figure 1). The maximum tensile strength for each specimen is over 455 MPa. To test our proposed approach, a run-to-failure dataset is constructed from four-point bending tests on the RC beams (Figure 2). Each four-point bending test is performed as follows: an RC beam is loaded equally with two concentrated loads. Supports are placed 2000 mm apart, center-to-center. The displacement rate applied by the actuator through the I-section of an 80-cm steel beam can be either 1 mm/s or 2 mm/s. This load is continuously applied on each RC beam until the beam is completely crushed. The vertical displacement is measured by a linear variable differential transformer (LVDT) located at the middle of the bottom surface (Figure 2).  
The data acquisition system is an eight-channel PCI-based AE system (PCI-8). This device is capable of simultaneous data acquisition, waveform processing, and data transfer up to 132 Mb/s in the 1-400 kHz range. Its channels are connected to eight low-frequency AE R3I-AST sensors mounted on the specimen's surface. These sensors have outstanding sensitivity and require no extra preamplifier on the long cable drive. In order to ensure that the inherent characteristics of the concrete are omitted, the sampling rate of 5 MHz is set.  
Figure 1: Schematic illustration of longitudinal section and cross section of the RC beam.
## 3 AI-Based Identification and Authentication
### Robustness in Challenging Conditions  
In industrial environments, biometric systems must contend with various challenges such as dust, noise, vibrations, and extreme temperatures. [PERSON] [42] discusses the environmental and security challenges associated with biometric systems, providing insights into how these challenges can be addressed to enhance the reliability and robustness of biometric technologies.  
[PERSON] et al. [43] examine the application of microsystems in harsh environments, including high temperatures, chemical disturbances, electromagnetic noise, and mechanical vibrations. They discuss the additional challenges these conditions present and the multidisciplinary approaches required to overcome them.  
[PERSON] et al. [44] discuss various challenges in designing biometric-based security systems, including environmental factors such as noise and non-ideal conditions, and propose feature extraction approaches to enhance robustness and accuracy.  
[PERSON] et al. [45] address the problem of noisy sensor data and other environmental challenges in biometric verification systems. They explore how multimodal biometric systems, which combine multiple sources of biometric evidence, can improve performance in challenging conditions.  
AI enhances the robustness of biometric systems in challenging conditions. Deep learning algorithms can adapt and perform consistently under such conditions [46]. For instance, in environments where fingerprint quality might be degraded due to dirt or damage, deep learning models can still accurately match fingerprints by focusing on deeper, more stable features rather than superficial ones [47].  
[PERSON] et al. [48] present a study on iris recognition in varying lighting conditions, demonstrating the robustness of deep learning models in adapting to challenging environments. The study shows that deep learning models can maintain high accuracy even under varying lighting conditions, unlike traditional algorithms that struggle in such scenarios.
## 2 Background and Related Work
### Related Work
Attribute-based encryption (ABE) is used to encrypt medical data, and then, the encrypted data are stored in the IPFS. ABE allows only authorized users to decrypt medical data in the IPFS. The hash values (data address of the IPFS) of the medical data are recorded in the Blockchain for data retrieval process and verification. Similar to the scheme in [20], EduRSS [21] combines Blockchain, storage servers, and encryption techniques to manage educational records in a decentralized manner. The encrypted original educational records are saved in distributed off-chain storage servers, while the hash information of the records is stored on the Blockchain. EduRSS utilizes smart contracts to regulate the data storage and sharing process. To comply with the privacy requirement of the General Data Protection Regulation (GDPR), which allows users to rectify or even erase their own data, several solutions have been proposed to delete and update data on the Blockchain. A redactable Blockchain based on hash function modification is proposed to re-write or compress the on-chain data on the append-only distributed ledger [22]. Due to secret trapdoor information, chameleon hash functions [23] can efficiently find hash collisions, which allow for redactable blocks without breaking the hash chain. To enable redactable off-chain data over the IPFS, a delegated content erasure is proposed to enforce complete content removal across the entire network [24]. The proposed protocol relies on a \"proof-of-ownership\" to ensure anonymous and censorship-resistant off-chain data storage, such that only a user is allowed to delete its own contents. Unlike the above redactable solutions, a pseudonymization-based approach [25] is proposed to satisfy GDPR as integrating with the Blockchain. The pseudonymization uses cryptographic hash functions for encrypting the date or pseudonymous identities for anonymity. Therefore, only users who have encryption keys of the pseudonymization can decrypt data or even eliminate content.
# Building Energy Retrofit Measures in Hot-Summer-Cold-Winter Climates: A Case Study in Shanghai
## 4 Building Retrofit Measures and Major Affecting Elements in Shanghai
### Key Elements Affecting Building Retrofit Measures in Shanghai
#### 4.2.4 Human Factors  
Human factors are added imperative components that should be considered for an adequate building retrofit measure. This includes schedules (occupancy, maintenance and management), comfort needs, activity and access to controls [81]. Changes in this factor will lead to changes in the energy use of buildings. [PERSON] et al. [82] estimated that occupant characteristics accounted for 4.2% of building energy use. Results from other studies have collaborated this finding, stating that changes in occupant behaviour, control and comfort range can significantly alter energy consumption [92, 93]. The merit of this factor is that it involves little or no capital cost.
# Learning-Based Online QoE Optimization in Multi-Agent Video Streaming
## 2 Related Work
However, they use model-predictive control, whereas we pose it as a learning problem and use reinforcement learning. The study in [46] considers a multi-user encoding strategy where the encoding schemes for each user vary depending on their network condition. However, the study uses a Markovian model and does not take the possible future network conditions into account. In [47], a future dependent adaptive strategy is considered, where the TCP throughput and success probability of a chunk download are estimated. Similar to us, the authors of [48] consider a reinforcement learning protocol to maximize the QoE for multiple clients. However, they use average client QoE at time \(t\) as a reward for time \(t\) and use deterministic policies learned from Q-learning [23]. We show that our formulation outperforms standard Q-learning algorithms by considering stochastic policies and rewards as a function of the QoE of the clients. Our work, by using a model-free deep-RL policy [15], aims to maximize the overall quality of experience of multiple agents. To measure the QoE, ref. [14] considers the web page loading time as a factor, ref. [49] tracks graphic settings, and [50] focuses on mobile networks, such that the signal-to-noise ratio, load, and handovers matter. More mapping methodologies can be found in the survey [51].