esa-sceva/satcom-chunk-collection huggingface dataset:
# A Force Measurement Method Based on Flexible PDMS Grating
## 5 Conclusions  
In this paper, a force measurement method based on PDMS grating is proposed. The fabrication of PDMS grating and property of PDMS are also introduced in detail. The following conclusions are drawn:  
1. The PDMS grating is cast from a commercial grating, and the fabrication process is easy to follow.
2. The measurement principle is transferring the force signal to the position shift of diffraction light spot, an optical signal. The relationship between light spot position and tensile force is linear (R\({}^{2}\)\(>\)0.9980). The sensitivity is \(\sim\)0.5-0.7 mm/N (mixing ratio 10:1 and 15:1, curing temperature 80 \({}^{\circ}\)C) and the accuracy is up to 0.05N.
3. Elastic modulus is one of the most important characters for PDMS. It is negatively correlated with the mixing ratio (Sylgard 184A to Sylgard 184B in weight), and positively correlated with the curing temperature. One can control the sensitivity of the proposed force sensor by tuning the elastic modulus.  
PDMS material has been widely used in the current research on flexible devices due to its excellent elastic property, good biocompatibility, and adaptability to the light. The proposed method based on PDMS grating is an optical sensing method, and therefore has potential for various specific force measurements in high electromagnetic interference applications. Moreover, the proposed method can be used to study the mechanical property of flexible material itself. This method is not only suitable for the uniform deformation measurement in this paper, but can also be extended to study non-uniform deformation such as large bending. By measuring different points on the sample surface, the stress/strain distribution can be obtained.  
A possible improvement of the proposed method is using reflection grating instead of the transmission grating. At the reflection mode, the sensor is easier to package and more suitable for measuring opaque sample. The challenge is coating the PDMS surface with smooth metallic reflective film, which needs further research efforts in the future.  
[PERSON] contributed to the main idea of this paper; [PERSON] performed the experiments, analyzed the data and wrote the paper; [PERSON] wrote the image processing codes; [PERSON] and [PERSON] reviewed and edited the paper. All authors have read and agreed to the published version of the manuscript.  
The presented work is supported by the following Grants: National Natural Science Foundation of China Grant No. U1713210; Shenzhen Fundamental Research Program Grant No. JCYJ20170818163928953  
The authors declare no conflict of interest.
# An Energy-Efficient Coverage Algorithm for Macrocell--Small Cell Network Systems
## 6 Conclusions  
The present study proposes and successfully demonstrates a novel coverage algorithm aimed at increasing the energy efficiency of macrocell--small cell two-layer network systems with novel system configurations. The proposed algorithm adjusts the power levels of all the SBSs in a given system, including the full deactivation of a previously active SBS or the activation of a previously inactive SBS, according to the dynamics of the given network traffic situation, thus reducing the power used by the SBSs, when appropriate, in order to limit any waste of energy. At the same time, by activating/deactivating the SBSs and adjusting their power levels at the most appropriate times, the proposed algorithm can simultaneously ensure that users are still provided with comprehensive wireless signal coverage. The results for simulations of various test scenarios indicated that the algorithm yields better system performance than two conventional approaches, in terms of its overall effects on the outage ratio of all the users, the total power consumption of transceivers at all the BSs, and the average transmission rate of all the users. The simulated power savings yielded by the proposed algorithm were particularly notable, as it garnered an improvement as high as 78% under the condition of light traffic volumes. Based on these findings, we believe that the proposed algorithm could yield considerable benefits if used in two-layer network systems, while also making substantial contributions to research, or the previous lack thereof, into the subject of coverage holes. Relatedly, we conclude that the proposed approach could be of substantial value/guidance to researchers and network operators engaged in the design of next-generation two-layer energy-saving networks. Moreover, by potentially providing substantial energy savings and contributing to environmental sustainability, this approach, or related approaches developed in subsequent related research, could help to mitigate the problem of global warming. Similarly, by providing for the judicious conservation and utilization of energy, this approach, or related approaches, may make the avoidance or reduction of various other types of environmental degradation possible.  
The author wishes to express his appreciation for the financial support provided by the Ministry of Science and Technology (MOST) of Taiwan, R.O.C., under the Contracts MOST 105-2628-E-019-002-MY3 and MOST 106-2221-E-019-011-MY2.  
**Conflicts of Interest:** The author declares no conflict of interest.
## 6 Simulation Results
### Time-Varying \(\beta\)  
Figure 5: Solutions of the simulated system (red lines) and their estimation (blue lines), for a constant transmission rate, where the top plots in subfigures (**a**,**b**) stand for the susceptible and infected normalized subpopulations, respectively, and the top plot in subfigure (**c**) stand for the transmission rate, considering that only the output new deaths is available.  
Figure 6: Solutions of the simulated system (red lines) and their estimation (blue lines), where the top plots in subfigures (**a**,**b**) stand for the susceptible and infected normalized subpopulations, respectively, and the top plot in subfigure (**c**) stands for the transmission rate, considering that only the output new deaths is available.  
With the estimations \(\hat{\beta}(t)\) and \(\hat{x}_{i}(t)\), and their respective maximum estimation error, the maximum estimation bounds have been calculated. The real values fall within the range specified by the maximum upper and lower estimation bounds, providing reliable worst-case and best-case scenarios, which are essential in safety and prevention applications (e.g., a controller designed to eradicate a disease considering that there are not any resource limitations). Although \(x_{3}\in I_{E}^{\hat{x}_{3}}\) in both simulations, the maximum estimation bounds \(\hat{x}_{3,E}^{\pm}\) are not informative since \(\hat{x}_{3,E}^{+}(t)=1\) and \(\hat{x}_{3,E}^{-}(t)=0\); see Figures 3 and 4.  
On the other hand, the approximated estimation errors give tighter bounds, and the values of \(\beta(t)\) and \(x_{i}(t)\) also lie within the region \(I_{E}^{\hat{\beta}}(t)\) and \(I_{E}^{\hat{x}_{i}}(t)\), respectively, for a wide range of \(t\). Even if the time-varying \(I_{\epsilon}^{\beta}(t)\) and \(I_{E}^{\hat{x}_{i}}(t)\) for \(i=1,2,3\) provide an approximated region where the real value will lie (i.e., the real values might go outside these regions for some \(t\)), it is affordable to consider \(I_{e}^{\beta}(t)\) and \(I_{E}^{\hat{x}_{i}}(t)\) instead of \(I_{E}^{\hat{\beta}}(t)\) and \(I_{\epsilon}^{\hat{x}_{i}}(t)\) in cases where it is not necessary to consider the worst/best scenario (e.g., if the controller's purpose is to minimize hospitalizations while saving resources). Overall, \(\hat{\beta}(t)\) and \(\hat{x}_{1}(t)\) with their respective maximum and approximated estimation bounds give valuable information. In contrast, due to the significant difference between \(\hat{x}_{3}(t)\) and \(x_{3}(t)\), in addition to the big values of the maximum and approximated estimation bounds, this method is not appropriate for \(x_{3}\) estimation.
# A Novel Global Key-Value Storage System Based on Kinetic Drives +
## 3 Background
### Comparison between Traditional and Kinetic KVS Systems  
The comparison between traditional and Kinetic KVS systems is shown in Figure 2 in detail. In traditional KVS, a storage server directly manages data stored in multiple disk drives. In Figure 1(a), a _key_ arrives first at the storage server, which finds the specific drive containing the key-value pair. Then, the storage server fetches those data and sends them to the user. Hence, the storage server is the intermediary for the actual data _value_ to go through.  
It is different for the Kinetic KVS system. To manage a set of Kinetic Drives and find which drive stores the specific key-value pair, a metadata server is needed. This metadata server only maintains a mapping between _keys_ and the IP addresses of the Kinetic Drives, as shown in Figure 1(b). We can see that when a _key_ arrives at the metadata server, it finds the IP address of the Kinetic Drive storing that key-value pair and returns the IP to the user. Then, the user directly contacts the specific Kinetic Drive to access the key-value pair. Hence, the actual data _value_ does not need to go through the metadata server to arrive at the user.  
Figure 2: Key-value store systems: traditional vs. Kinetic [16].
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 4 Evolution of Communications Technologies for Telemedicine  
Communication technologies for telemedicine implement the transfer of captured data from sensors placed on the human body, and can be configured to form sensor network systems. The development of communications technologies has greatly aided the evolution of telemedicine. The invention of the electrical telegraph and Plain Old Telephone Service (POTS) has been linked with the beginning of modern telemedicine. With the electrical telegraph, medical information could be transmitted as text more easily and speedily than sending a letter by mail, which used to be the alternative means of communicating. The telegraph was first used in the medical context by the military, in teleconsultation, ordering of medical supplies, and as a means of transmitting other health information.  
Communication technology further developed beyond the use of the POTS, which merely provided audio services. Over the years, communication in telemedicine has progressed from wired technology to wireless technology [86]. The introduction of Public Switched Telephone Network (PSTN), Integrated Services Digital Network (ISDN), and Voice over Internet Protocol (VoIP), all of which have the capabilities of transmitting data and voice over telephone lines, have enhanced telemedicine tremendously. Moreover, these technologies have very good transmission data rates; for instance, ISDN can provide a data rate of 128 kbps. The advent of the internet has led to a massive increase in information flow. The sudden rise in internet patronage in the 1990s provided a massive boost to telemedicine services and applications. The use of the internet has given rise to cloud computing, fast and reliable transmission of medical information, and videoconferencing.
## 2 Methods
### Multi-Channel Gradient Fusion  
Although PCA reconstruction reduces channel-specific noise, residual high-frequency noise may persist in the point cloud. A secondary denoising step using gradient computation is implemented. First, 3D Gaussian filtering [22] is applied to the RGB channels of the point cloud to suppress noise through weighted averaging on the basis of spatial proximity and color similarity between neighboring points while preserving edge structures.  
Next, multi-channel gradient computation is performed on the RGB channels by use of a modified Scharr operator adapted for 3D point clouds. Horizontal and vertical gradients are calculated within the 2D projection plane of the point cloud. Compared with single-channel gradient calculation, multi-channel calculation can capture edge information only existing in specific color channel by analyzing gradient of each channel independently, so as to avoid missed detection caused by relying on single channel. At the same time, the gradient responses of multiple channels are synthesized, the channel information with the least interference is automatically selected, and the maximum values among the channels are fused. The gradient magnitude and direction for each RGB channel are computed independently. To fuse multi-channel gradients, the maximum gradient magnitude across channels is selected at each point, ensuring robust edge preservation under varying lighting and color conditions. This fusion strategy prioritizes the channel with the most significant color contrast, improving detection accuracy in complex scenes.  
These preprocessing steps collectively ensure that subsequent edge detection algorithms operate on high-quality and consistent gradient information, laying the foundation for generating precise and continuous edge structures.
## 4 Software-Bus Components
### Communication Protocols  
In order to be universally applicable, the SBT supports a large number of communication protocols. This stack is an initial set-up that will be extended continuously. As a general concept, the communication protocol is abstracted in a way that interfaces take data points, wrap them in packets of the corresponding communication protocol and transmit them via a hardware connector. This approach ensures independence of data points from the communication aspect. The same data point can be created, sent or received on different devices via different protocols. This approach provides device level interoperability. As protocols are available as functional modules, the implementation time is reduced as well. Currently, the following protocols are implemented:  
Figure 3: Possible use case scenarios of SBT.  
* WiFi
* TCP/IP
* Bluetooth
* FC
* SPI
* UART  
This collection shows a focus on microprocessor-boards and their communication. However, the implementation of protocols from IIoT scenarios, such as Modbus, CAN, and Profibus are currently pending.
## 4 Evaluation
### Scalability Analysis  
To evaluate the scalability of the proposed adaptive user interface system, we conducted a simulation-based stress test involving synthetic data for larger student cohorts of 300 and 500 learners. Each simulated student session followed the same pipeline as the original study: fuzzy classification based on exercise time, adaptive UI changes, and reinforcement learning-driven threshold adjustments.  
The system was tested on a standard server (Intel i7, 16 GB RAM) and measured on the basis of three key metrics:  
* Average processing time per session (including fuzzy inference and RL update),
* Memory footprint per student instance, and
* System throughput (number of students processed per minute). Results indicated the following:
* For 300 students, the average session processing time was 0.28 s with stable memory usage (~35 MB per student).
* For 500 students, average time slightly increased to 0.31 s, with memory remaining below 40 MB per user.  
The system demonstrated near-linear scalability, benefiting from the modular and stateless design of both the Fuzzy Inference Module and RL Optimization Module. Since each student session is processed independently, the architecture supports parallelization and distributed deployment without major refactoring.  
These results suggest that the system can effectively scale to support larger classrooms or massive open online courses (MOOCs). Future work will explore deployment in cloud-based environments to validate live multi-user scalability and load-balancing strategies.
## 2 Materials and Methods  
### Dataset  
In this retrospective, non-consecutive, study, DBT images acquired from patients at our Institution between May 2018 and December 2019 were retrieved, retrospectively evaluated and collected for a radiomic-based classification study to predict malignant vs. benign breast calcifications with low FP ratio. Patient consent was waived by the Ethical Committee due to the retrospective nature of this study. In particular, the study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethical Committee of Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico in Milan (protocol code Tomo-AI; protocol-ID 1666; approved on 14 October 2020).  
DBT images were acquired using two full-field digital mammography systems with tomosynthesis (Selenia Dimensions; Hologic Inc., Marlborough, MA, USA). At the time of the study, both the digital mammography systems machines were working within the manufacturers' specifications and EC quality control regulations. Both the cranio-caudaland the mediolateral-oblique projection on patients' breasts were reviewed by an expert breast radiologist with more than 10 years of experience, and the projection in which calcifications were most evident was ultimately analyzed to provide the diagnosis.  
Inclusion criteria for the study were: patients with higher than normal risk (e.g., familiar risk) with suspicious microcalcifications detected on DBT by an expert radiologist reader, classified by the radiologist according to the Breast Imaging-Reporting and Data System (BI-RADS) radiological classification [21], diagnosed by the radiologist as \"suspected of malignancy\" (positive at DBT), then sent to DBT-VAB for histopathological reports and followed up for 1-2 years.  
DBT-VAB was performed by three dedicated breast radiologists (with 5-15 years of experience) using a prone breast biopsy system (Affirm Prone and Eviva; Hologic Inc., Marlborough, MA, USA) in combination with a 9-gauge needle for tissue sampling, acquiring at least 12 specimens for each patient.  
The number of DBT images were selected in order to fulfill a 2:1 ratio of benign to malignant ratio of calcifications as diagnosed at histopathological reports (and clinical follow up) in order to properly train the model on low false-positive performance.
# Joint Image Compression and Encryption Using IWT with SPIHT, Kd-Tree and Chaotic Maps
## 2 Related Works  
There exist many image compression and encryption algorithms. However, to achieve robust security and compression together is still a challenging task because both are contradictory things. In this section, we will discuss SPIHT based image compressions, chaotic map-based image encryptions and some joint image compression and encryption schemes.
## 4 Proposed Algorithm
### Proposed Machine Learning-Based Opportunistic Routing (OR) Protocol
#### 4.2.2 Network Model  
The N sensors are distributed in a square area of size \(500*500\) square meters. This network formed a graph G = (N, M), with the following possessions:  
* N = {N\({}_{1}\), N\({}_{2}\),..., N\({}_{n}\)} is the set of vertices representing sensor nodes.
* M is considered to be a set of edges representing the node-to-node links.
* The neighboring list NBL(N\({}_{i}\)) consists of nodes that are in the direct link to the N\({}_{i}\).
* The data traffic is assumed to be traveling from the sensor nodes toward the base station.
* If a packet delivery is successful, then the acknowledgment (ACK) for the same is considered to travel the same path back to the source.
## 3 Datasets
### Gridded Precipitation Products (GPPs)
#### 3.1.1 Tropical Rainfall-Measuring Mission Multi-Satellite Precipitation Analysis (TMPA)  
The Tropical Rainfall-Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) is among the first satellite-based GPPs with fine spatial resolution covering the globe (i.e., 50\({}^{\circ}\)S-50\({}^{\circ}\)N). This dataset includes precipitation estimates at a 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) latitude/longitude spatial resolution in a sub-daily (i.e., three hourly) time scale [14]. The TMPA incorporates precipitation estimates from various satellites, as well as in situ observations. Precipitation estimates are derived from the TRMM Microwave Imager, Special Sensor Microwave Imager, Advanced Microwave Scanning Radiometer-Earth Observing System, Advanced Microwave Sounding Unit-B, Geostationary Operational Environmental Satellite, and METEOSAT [14; 68; 69]. Precipitation estimates are then enhanced by the TRMM Precipitation Radar and in situ rain gauges from the Global Precipitation Climatological Center (GPCC). Further and detailed information about TMPA GPP can be found in [14]. In this study, TMPA precipitation data from 2000 to the end of 2019 were considered due to their unavailability in 2020.  
Figure 1: (**a**) Geographical location of Iran, along with spatial distribution of synoptic stations throughout the country in 2020, and (**b**) the annual accumulated precipitation in Iran produced using kriging interpolation and in situ measurements with a spatial resolution of 0.025\({}^{\circ}\).
# Low Power Wide Area Network, Cognitive Radio and the Internet of Things: Potentials for Integration
## 7 Challenges/Future Directions of CR-LPWAN
### Upper Layer Challenges
#### 7.2.3 Security Challenges  
IoT applications often involve the transfer of confidential information, for example, acquiring medical vitals in telemedicine, or within smart city networks, wherein utility bills are transferred. These applications may require secured links before communication can be initiated. This may be realized, albeit at the expense of increased complexities, energy consumption rates, and costs, which will limit many IoT use-cases [154]. Thus, attacks such as unauthorized use of personal data, data leaks or spoofing, hacking, denial of service, and flooding, are potential challenges that must be addressed in CR-LPWAN networks. In fact, the use of CR in LPWAN introduces the specific security problem of primary user emulation (PUE) attack, wherein attackers could emulate bona fide PU transmitters in order to deprive other CR-LPWAN devices access to white spaces [155]. Other specific security challenges may include:  
1. **Light weight Security Protocols:** There are a number of existing security protocols for traditional wireless networks and IoT networks [156], which cannot be deployed for CR-LPWAN applications because of their complexities. It is thus required to develop light weight security protocols to ensure low power consumption rates, and reduced complexity in CR-LPWANs.
2. **Software Vulnerability:** It is often expected that the initial version of most software are plagued by programming bugs known as software vulnerabilities [154]. It is essential to reduce as much as possible the number of bugs in CR-LPWAN software, e.g., spectrum mobility and management software, to reduce the risk of back-door sabotage, which may lead to malicious attacks and network downtimes if left unchecked.
3. **Malware:** The promising and open spectrum-access nature of CR-LPWAN suggests that it is a potential breeding ground for malware attacks. Malicious attackers are constantly on the prowl looking for possible loopholes to exploit. While malware attacks against IoT software may seem far fetched, it is worth noting that Symantec has declared the detection of the first piece of IoT malware (a worm) termed \"Linux.Darlloz\" [157]. Others include Mirai, which turn networked devices running Linux into remotely controlled bots for large-scale network attacks. These discoveries imply that CR-LPWAN devices and networks are not exempted from possible malware attacks, which demand new research measures to improve security.
# A Forwarding Latency Optimization Method for Software Data Plane Based on Spin-Polling
## 5 Design and Implementation  
In this section, we first give the reasons why the packet forwarding latency can be optimized and then design a set of packet latency optimization strategies according to the performance utilization of the CPU.