esa-sceva/satcom-chunk-collection huggingface dataset:
# Harnessing FPGA Technology for Energy-Efficient Wearable Medical Devices
## 5 Power Optimization Strategies
### Exploiting FPGA Features
#### 5.4.3 Fine-Grained Power Modes  
FPGAs allow granular control over power modes for shutting down any module not required for a given task. Such control over power modes reduces not only dynamic power consumption but also static power consumption. FPGAs consume static power even when idle due to transistor leakage [122]. Fine-grained power modes minimize this static power by selectively gating unused components. A similar approach was used in [123] to put a complex module in sleep mode until required. From analysis of the patient-specific seizure patterns, the authors noticed biases in distribution over time. Therefore, they created two different, simple, and complex models for seizure detection. During the time of day when the likelihood of occurrence of seizures is low, a simple model can be activated to lower the power consumption. Since the likelihood of occurrence over the time of a day is extremely low, the system will mostly remain in low power mode, and much power can be saved.  
Implementing power down modes for inactive components can result in significant energy savings. For instance, placing communication interfaces, unused sensors, or an associated ADC [124] into sleep mode can reduce the system's power consumption, thereby extending battery life in WMDs. However, there are various levels of power down modes, such as low power, ultra-low power, sleep mode, and deep sleep mode. It is crucial to consider the wake-up time of these modes to prevent missing critical events in continuous health monitoring.  
The advantages and constraints associated with these optimization strategies are summarized in Table 6 along with their descriptions.
# RF Frequency Selective Switch by Multiple PMIM Conversions
## 4 Discussion  
In summary, we proposed a technical approach to realize a RF frequency selective switch with broadband and a high-frequency spectral resolution. In our scheme, multiple phase modulation to intensity modulation conversions was incoherently combined together in the optical frequency domain, and stable reconfigurable RF spectrum processing units were then obtained by simply adjusting each tooth power of the optical frequency comb. In the experiment, we used an optical frequency comb containing 21 teeth and a single optical ring resonator to realize the reconfigurable filtering and frequency selective switching of RF spectrum in 9.5-10.5 GHz, with a resolution of 50 MHz. Our technical advantage is that the low-resolution optical WSS can be mapped to a high-resolution RF FSS through the amplification effect of the Vernier effect. Further, if a second optical WSS is used to connect its synthetic end with our setup, a MIMO RF processor can be obtained.  
Figure 5: **(a)** The frequency response of the RF FSS when two ports are used and each outputs five RF channels. (**b**) Five ports are used and the RF FSS outputs two RF channels in every port. (**c**) The RF FSS is set as an interleaver.  
**Author Contributions:** Conceptualization, W.H. and Y.P.; methodology, W.H. and Y.P.; software, W.H.; validation, W.H., Y.P. and S.W.; formal analysis, W.H.; data curation, W.H. and X.L.; writing--original draft preparation, W.H.; writing--review and editing, Y.P. and S.W.; supervision, X.L.; project administration, X.L.; funding acquisition, W.H. and S.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Natural National Science Foundation of China, grant number 61901428 and 62001440.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Instrumentation
### Tilt Angle Measurement  
The tilt angle of the PV panel is controlled through a hinged lever mechanism. As illustrated in Figure 3b, a stepper motor (g) is utilized to drive the ball screw (h), which, in turn, moves its nut (i). The nut is connected to two casters (j) positioned on either side. These casters are linked to the PV panel via two strut bars (k), allowing for the vertical movement (lifting up or down) of the PV panel around two hinges (l).  
The working principle of the hinged lever mechanism can be summarized as depicted in Figure 4. At each end of the strut bar, there are two hinges denoted as B and C points, which serve to assemble the PV panel and the casters. Meanwhile, the two hinges (l) that lever the PV panel by the chassis are denoted as point A.  
The workspace of the hinged lever mechanism can be described using the following equation:  
\[a\cos\alpha+\sqrt{b^{2}-(a\sin\alpha+d)^{2}}=c \tag{5}\]  
where \(\alpha\) is the tilt angle; \(a\) is the length of the PV panel-bearing frame, 245 mm; \(b\) is the length of the strut bars, 275 mm; \(d\) is the distance between of the hinges (l) and the caster (j) along the vertical axis, 7.5 mm; and \(c\) is the position of the caster along to horizontal axis with respect to the hinge (l), located at point A. It is important to note that the parameter \(c\) is a time variable depending on the tilt angle \(\alpha\) while the others are all constants.  
It can be seen from Equation (5) that the tilt angle can be adjusted by controlling the position of the caster and, in turn, the rotary position of the stepper motor. To avoid  
Figure 4: Kinematic diagram of hinged lever mechanism.  
Figure 3: CAD model of slip resistance test apparatus with (a) a chassis, (b) a PV panel, (c) a displacement mass, (d) a weight-bearing frame, (e) an electronic load cell, (f) adjustable water sprayer nozzles (a), (g) a stepper motor, (h) a ball screw, (i) a ball screw nut, (j) casters, (k) strut bars, and (l) hinges (b).  
physical damage, proximity sensors are attached at the terminals of the ball screw to force an emergency stop in the stepper motor in case the position of the caster exceeds the limitation of the workspace. Figure 5 shows the relationship between the theoretical tilt angle and the experimental tilt angle of the PV panel. The theoretical tilt angle, shown by the squared-dot line, is obtained by using the rotary position feedback of the stepper motor, the ball screw pitch, and Equation (5) with given constants. The position of casters varied from 280 mm to 525 mm with an increment of 5 mm to achieve the tilt angle of the PV panel ranging from 5.9\({}^{\circ}\) to 53.3\({}^{\circ}\). The experimental tilt angle, shown by the circle-dot line, is recorded by a calibrated inclinometer. The results showed that the theoretical values and the measured values are a good fit with an acceptable difference (MSE = 0.88).
# Rural Healthcare IoT Architecture Based on Low-Energy LoRa
## 6 Discussion
We found that the most commonly addressed approach is minimizing energy usage by tweaking the LoRa parameters. We identify and provide scenarios for other means to reduce energy usage, focusing on the device's smart duty cycle. While modern microcontrollers, such as the ESP-32, can have multiple sleep stages, nano-Ampere timers can be used to push this approach even further. The energy consumption of the sensors must also be minimized by a balanced approach of reducing the duty cycle of the sensors while still collecting the data when needed. Variable scanning resolution is used to ensure valuable data is collected and wasteful cycles are minimized. The battery extension that we have achieved is in the range of tens of times, depending on the utilization profile of the microcontroller, for low utilization even up to 80 times longer battery life [50]. In [59], the authors have achieved 95.2% reduction, and they measured a sleep mode current of 33 nA. While we have achieved 99% reduction, this is due to the higher current that our chosen microcontroller draws during sleep. A similar decrease in inactive current by 90-95% was shown in [60]. In [61], the authors have achieved 40 times battery extension using an event-driven system for continuous and long-term operation of a wireless sensor to monitor civil infrastructure given a 1% event probability. While direct comparison cannot be measured due to the difference in the power profile of the IoT microcontroller and the implementation, our results are comparable to the ones published in the literature. While the proposed architecture allows rural healthcare to be implemented, the constraints of low network bandwidth and the need to reduce the active time of the IoT nodes limit the capabilities of the system. For example, sensors that generate a large quantity of data cannot effectively be used in the proposed architecture. In addition, the proposed architecture is very sensitive to bugs in firmware and software that prevent the device from booting correctly or entering a low power state. Such bugs could drain the battery rendering the node unusable until it is serviced, decreasing the benefits of the proposed architecture. We expect the most challenging aspect of the deployment of the proposed architecture to be the initial deployment, specially the communication hub that would require power supply and battery backup to ensure reliable operation.
# Investigation of Static and Kinematic Surveying Performance of Handheld GNSS Receiver +
## 4 Conclusions  
In this study, the usability of the Garmin GPSMAP 66 sr handheld receiver in geodetic measurements was tested, and its static and kinematic positioning performances were investigated with the relative method and PPP technique. The results of the relative static positioning method showed that as long as the ambiguities were fixed, 2D positioning and height accuracies at the few centimeter-levels can be achieved with an even shorter occupation time. When looking at the relative positioning results of the kinematic test (walking part), it was seen that a couple of decimeters of accuracy for 2D-position and height components were obtained. It has also been observed that the use of multi-GNSS and multi-frequency data in PPK solutions has a significant and positive effect on accuracy performance.  
Static data processing results performed with the PPP technique showed that positioning (2D and height) at the meters-to-decimeter level was possible with the Garmin receiver. Additionally, it has been observed that Kinematic PPP results have too low accuracy to be used in geodetic applications. It should be remembered here that both static and kinematic CSRS-PPP solutions use only L1 frequency.  
According to the overall results obtained from the study, it was shown that the Garmin handheld GNSS devices can replace high-cost geodetic-type GNSS receivers in certain areas and can be used in many geodetic measurement applications as low-cost and high-accuracy positioning tools. It was also understood that the promising developments in alternative positioning platforms provided a different perspective on positioning with GNSS.  
All authors contributed to the preparation of this paper. Conceptualization, R.M.A. and S.E.; Methodology, R.M.A. and S.E.; Software, B.M. and M.Y.B.; Validation, B.M. and M.Y.B.; Formal Analysis, S.E. and B.M.; Investigation, R.M.A., S.E., B.M. and M.Y.B.; Resources, R.M.A. and S.E.; Data Curation, B.M.; Writing--Original Draft Preparation, R.M.A., S.E. and B.M.; Writing--Review and Editing, R.M.A. and S.E.; Visualization, B.M. and M.Y.B.; Supervision, R.M.A. and S.E.; Project Administration, R.M.A. and S.E.; Funding Acquisition, R.M.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Scientific Research Projects Department of Istanbul Technical University, grant number FHD-2023-45312.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available upon request from the corresponding author.  
**Acknowledgments:** The authors would like to thank the Istanbul Water and Sewerage Administration (ISKI), Istanbul Metropolitan Municipality, for supplying the GNSS data.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 6 Conclusions  
The simulation-based proposals analyzed in this paper for the physical layer of PRIME 1.3.6, PRIME 1.4, and G3-PLC technologies are proven to improve their response against disturbances present in the LV grid. PLC performance is enhanced with a combination of implementation improvements and modifications to the standards that could be included in future versions of PLC protocols.  
Advanced metering infrastructure (AMI) deployments evolve to applications of higher complexity and innovation. PLC technologies that will enable these applications require robust testing and thorough performance analysis and evolution. The Virtual PLC Lab used in the present study has proven to be much more efficient than the conventional laboratory analog approach, saving a considerable amount of time and resources.  
PRIME PLC technology performance and robustness increase when the improvements under analysis are introduced. A PRIME equalizer based on the spline curve model is more stable than the original polynomial curve model when adapting to channel responses impaired by tonal noises. Reed-Solomon makes PRIME modulation schemes without a convolutional encoder usable for real field scenarios. Reed-Solomon encoding is especially interesting for D8 PSK without convolutional encoding.  
The G3-PLC standard tone map increases the link budget for in-band tonal noises and its benefit is higher for high-density modulation schemes. The impact of the modification to the standard tone map using power reallocation in the link budget is related to the number of '0's in the tone map. The header robustness limit can be reached as the tone map is applied to the payload only, and CENELEC out-of-band emission levels [28] affected by the tone map with power reallocation must be considered for the hardware design of this option.
## 3 Image Sensor: Power Consumption Analysis  
After describing the proposed image sensor design and coding the operating software, we will proceed to perform a detailed power consumption analysis in order to estimate its autonomy with standard battery cells. For that purpose, first we will perform a timing profile of the basic operations executed in the running mode using different image resolutions. Then, we will perform a set of power consumption measurements of sensor node, to determine the required energy in standby and running modes. In running mode we will measure the power consumption of every single basic operation, to determine the most energy-intensive operations. Finally, taking in mind the power consumption analysis we will estimate the image sensor autonomy with standard battery units, varying the image size (\(80\times 64\), \(160\times 128\), \(320\times 240\), \(640\times 480\)) and the image capture period (0.5, 1, 3, 6, 12 and 24 h between two consecutive images).
# Multi-User Joint Detection Using Bi-Directional Deep Neural Network Framework in NOMA-OFDM System
## 4 Proposed Deep-Learning Model  
In this section, the proposed Bi-LSTM model input data preparation and model structure with its operation in the NOMA OFDM framework are discussed. Then, the offline training and online testing mechanism of the trained model are presented.
## 3 Methodology
### Evaluation Metrics  
The models were evaluated using the following metrics.  
* Topic Coherence: Interpretability and topic coherence are critical attributes of an effective topic model. A topic is defined as a distribution of words that collectively represent a single semantic theme. To determine whether or not the words within a topic are meaningful to humans, their coherence must be evaluated--specifically, the degree to which the words are semantically related. In this study, we assessed topic coherence using NPMI, a metric that ranges from \(-1\) to \(1\), allowing for direct comparison with findings from previous research [28].
* Topic Diversity: This metric measures the semantic variety of the topics produced by a model. Topic diversity can be quantified by calculating the proportion of unique words among the top 25 words across all topics. The higher the score on this metric, the more diverse the topics are, suggesting a successful topic model. Conversely, a low diversity score implies that the topics are redundant, reflecting the model's inability to effectively separate the different themes within the text corpus. It is important to consider how the number of topics chosen for the model influences this diversity. Selecting too many topics may lead to a convergence of similar themes, resulting in a loss of distinctiveness. On the other hand, opting for too few topics may yield overly broad and less interpretable topics [15].
* Perplexity: This is a popular metric for evaluating probabilistic models like LDA, where it measures how well a probability model predicts a sample. However, its applicability is limited when it comes to methods like NMF and BERTopic because these methods are based on different underlying principles and computations [15].  
It is crucial to recognize the balance between perplexity and coherence; optimizing for one may detrimentally impact the other, as suggested in [28]. Additionally, the choice of hyperparameters, including the number of topics, can significantly influence coherence metrics [15].
# Finding Patterns in Signals Using Lossy Text Compression
## 2 Problem Statement
### Lossy Compression  
The previous subsection discussed exact (non-lossy) compression. However, given a string \(P\), our goal is intuitively to compute a string \(Q\) which is a \"lossy compression\" of \(P\) in the sense that: (a) \(Q\) is similar (not necessarily identical) to \(P\), and (b) \(Q\) takes less space in memory than \(P\).  
Of course, we can trivially define \(Q=P\) so that the similarity of \(P\) and \(Q\) will be maximized, but then there will be no compression or memory saving at all. On the other hand, we can define \(Q=1^{n}\), which will minimize the compression cost of \(Q\) (since it is just \(n\) occurrences of the digit 1), then the similarity cost to \(P\) will be very high. In other words, there is a trade-off between these two costs or goals. For a proper lossy compression problem we thus need to define, in addition to the compression cost of the previous section, a similarity and overall cost functions as follows.  
**Similarity cost**. Such a function \(\operatorname{scost}(\cdot,\cdot)\) maps every pair of strings \(P\) and \(Q\) of the same length into a score (real number) \(\operatorname{scost}(P,Q)\) that measures how much the strings are different; i.e., how much \(Q\) is a good approximation to \(p\). In this paper, \(\operatorname{scost}(P,Q)\) will be the number of indices \(i\in\{1,\cdots,n\}\) which has a different corresponding letter in \(P\) and \(Q\), also known as the _Hamming distance_[26] between \(P\) and \(Q\). For example, if \(P=ababcb\) and \(Q=ababab\), then \(\operatorname{scost}(P,Q)=\operatorname{scost}(ababcb,ababab)=1\) since only the 5 th letter is different: \"c\" for \(P\) and \"a\" for \(Q\).  
**An overall cost function.** This function \(\operatorname{cost}(\cdot,\cdot)\) assigns an overall score for a pair of string that measures the trade-off between the similarity rate \(\operatorname{scost}(P,Q)\) and the compression rate \(\operatorname{cost}(Q)\). For simplicity, we use the natural goal of minimizing the \(sum\) of similarity cost and compression cost,  
\[\operatorname{cost}(P,Q)=\operatorname{scost}(P,Q)+\operatorname{rcost}(Q).\]  
For example, if \(P=ababcb\) and \(Q=ababab\), then the overall cost is  
\[\operatorname{cost}(P,Q)=\operatorname{scost}(P,Q)+\operatorname{rcost}(Q)=1+3=4,\]  
compared to the original cost  
\[\operatorname{cost}(P,P)=\operatorname{scost}(P,P)+\operatorname{rcost}(Q)=0+6=6.\]  
The second problem statement is then: given \(S\), how can one compute a lossy compression that is both small and decompressed to a similar string as the given one.  
**Problem 3**.: _Given a string \(P\), compute a string \(Q\) that minimizes_  
\[\operatorname{cost}(P,Q)=\operatorname{scost}(P,Q)+\operatorname{cost}(Q),\]  
_over every string \(Q\). Here \(\operatorname{rcost}(Q)\) is the (optimal) RRLE compression cost of \(Q\) as in Definition 1, and \(\operatorname{scost}(P,Q)\) is the similarity (Hamming) cost._
## 2 Swarm Intelligence Algorithms
### Grey Wolf Optimizer (GWO)  
The Grey Wolf Optimizer (GWO) is an SI algorithm inspired by the hunting behavior of grey wolves in nature. The GWO imitates the social hierarchy and cooperative hunting strategies observed in wolf packs to guide the search for optimal solutions [9].  
In the GWO, a population of candidate solutions, represented as grey wolves, explores the search space by adjusting their positions and mimicking the hunting behaviors of alpha, beta, and delta wolves. GWO implementation is shown in Algorithm 4[14].  
```
1:\(n\): number of wolves
2:\(d\): dimension of the search space
3:\(t_{max}\): maximum number of iterations
4:\(a\): alpha coefficient
5:\(b\): beta coefficient
6:\(c\): delta coefficient
7:\(x_{i}\): position of wolf \(i\)
8:\(f(x_{i})\): fitness of wolf \(i\)
9:\(x_{i}^{*}\): best position of wolf \(i\)
10:\(f(x_{i}^{*})\): best fitness of wolf \(i\)
11:\(x^{*}\): optimal solution
12:Function\(\mathrm{GWO}(\,)\)
13:for\(i=1\) to \(n\)do
14: Initialize \(x_{i}\) randomly
15:endfor
16:while\(t<t_{max}\)do
17:for\(i=1\) to \(n\)do
18: Calculate \(a\), \(b\), and \(c\)
19: Update position: \(x_{i}\gets x_{i}+a(x_{i}^{*}-x_{i})+b(x_{i}^{b}-x_{i})+c(x_{i}^{c}-x_{i})\)
20:endfor
21:for\(i=1\) to \(n\)do
22:if\(f(x_{i})<f(x_{i}^{*})\)then
23:\(x_{i}^{*}\gets x_{i}\)
24:endif
25:endfor
26:\(t\gets t+1\)
27:endwhile
28:\(x^{*}\gets x_{best}\)
29:\(f^{*}\gets f(x_{best})\)
30:endFunction
```  
**Algorithm 4** GWO Pseudocode
# Behavioural Verification: Preventing Report Fraud in Decentralized Advert Distribution Systems
## 6 Evaluation
### User Privacy  
User privacy is ensured through the application of encryption, anonymous connections and the employment of Agents and Commissioners who serve the role of partially trusted proxies. The Advert Request Message (\(ARM\)) which is sent by the user to request adverts is encrypted with the Broker's public key \(B_{PuK}\). The requested adverts are placed in an Advert Delivery Message (\(ADM\)) that is encrypted with an encryption key \(U^{\prime}_{EK}\) which was provided by the user within the \(ARM\). This ensures that the Agent who serves the user has no access to neither the requests nor the ads. Similarly, the Ad-Reports of the user's \(ARC\) are also encrypted with the Broker's public key \(B_{PuK}\) which ensuresprivacy against the serving Commissioner. The Broker does not learn the identities of the user, the Agent or the Commissioner as the exchange of information takes place over an anonymous connection. The same anonymous connection is also in place when the user collects a Checkpoint Block (_CB_) from an Ad-Dealer within his or her proximity. The request is encrypted with the Ad-Dealer's public key \(D_{PuK}\) and contains a user-generated encryption key \(U_{EK}\) which is used by the Ad-Dealer to transfer the _CB_. This ensures that an eavesdropper cannot gain access to the user's information nor spoof the identity of the Ad-Dealer in order to transmit a fake _CB_.
# An Optimized Planning Tool for Microwave Terrestrial and Satellite Link Design
## 3 Tool Description
### Satellite Links
#### 3.5.4 Radio Characteristics  
Similarly to terrestrial links, in this step, the user is required to input the radio communication characteristics used in the simulated link. Although maintaining the layout of the terrestrial links, for satellite links, the user can define different frequencies for the ascending link (uplink) and descending link (downlink).