esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Research Objectives and Methodology  
This paper aims to discuss the main challenges and risks, from a data protection point of view, when automated decision-making systems based on ML systems are employed, thus providing the necessary background as a survey and paving the way for subsequent research that is needed. To understand the data protection risks, the paper will be based on the legal provisions stemming from the European legal framework, and, especially, the GDPR, which, as it has been stated (see, e.g., [18]), can serve as a useful model for other regulations to follow in terms of rights and principles. More precisely, this paper aims to address the following research objectives:  
1. A classification and description of the privacy and data protection threats when using ML algorithms for automated decision-making systems, associating each of them with the nonfulfillment of specific legal provisions;
2. A review of the data protection engineering techniques that have been proposed to alleviate the aforementioned privacy and data protection threats;
3. Further investigation of the application of differential privacy (DP) techniques to the training dataset via exploring the effect that each parameter of an algorithm has when evaluating both the accuracy of the output as well as the privacy achieved for the training dataset.  
On the basis of the above, the methodology and the contributions of this paper are as follows:  
1. First, important results in the field of data protection attacks on ML systems are surveyed in conjunction with relevant data protection engineering techniques that have been proposed. In this respect, for each known privacy threat in ML systems, we associate the relevant provision of the GDPR that seems to not be in place, thus establishing direct connections on how the nonfulfillment of legal requirements yields specific weaknesses (allowing effective privacy attacks) in practice;
2. Additionally, based on the work in [15] that applies DP to the training dataset of deep learning algorithms, some new results are also given, based on a set of extensive experiments relying on the above work that we carried out, indicating that there is still much room for further research since we, indeed, manage to achieve better accuracies for the algorithms by appropriately configuring several hyperparameters. However, we also address the challenge of what should be considered \"acceptable\" accuracy when we refer to decision-making systems concerning individuals.  
Our preliminary analysis reveals that the research outcomes need to be meticulously considered by the relevant stakeholders when ML systems are deployed, i.e., the corresponding algorithmic impact assessments should be conducted based on deep knowledge of whether and how State-of-the-Art privacy-enhancing technologies, indeed, suffice to alleviate data protection risks. The paper concludes with future steps that are suggested concerning both research as well as governance aspects.
# Harnessing the Cloud: A Novel Approach to Smart Solar Plant Monitoring
## 4 Our Proposed Architecture for Smart Solar Plant
### Perceptual Layer
#### 4.1.2 Sensors  
Real-time data are the key component of any smart monitoring system, as such systems make smart and meaningful decisions about the system's operation and performance based on the real-time data. For solar plant monitoring, various sensors are integrated with Arduino Nano depending upon the component (solar panel, inverter, or battery). Some commonly sensed parameters range from current, voltage, panel temperature, ambient temperature, humidity, irradiance, and dust particles. Sensed parameters can be transmitted to servers at user-defined intervals (ranging from 1 to 30 min, hours, or days).The most commonly used sensors listed in the existing literature are presented in Table 4.
## 3 Methodology
### Implementation  
In order to evaluate the different baselining methodologies, a number of steps need to be taken. These steps follow the flowchart presented in Figure 2. The steps are briefly described below, after which a more elaborate description is provided on the implementation of the curtailment process and of the different baselining methodologies.  
* The expected day-ahead PV output is determined, using weather forecasts and the PV model described in Section 3.4.
* The PV output profile is compared to a pre-set congestion threshold to determine the required flexibility (curtailment), and its duration.
* The actual PV output profile is generated, using weather measurements and the PV model described in Section 3.4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Solar Irradiance [W/m\({}^{2}\)]** & **Temperature [\({}^{\circ}\)C]** & **Wind Speed [m/s]** \\ \hline
1–8 June & M & F & M & F & M & F \\ \hline minimum & 0 & 0 & 10.2 & 10.8 & 0.0 & 1.0 \\ maximum & 889 & 864 & 31.3 & 31.7 & 10.0 & 9.0 \\ average & 233 & 258 & 18.4 & 19.4 & 3.4 & 3.5 \\ \  
* The actual PV output profile is cross-referenced with the expected curtailment profile, correcting the curtailment profile for the actual behaviour on the day of flexibility delivery. The output of this ex-post measurement-based correction consists of synthesised measurement profiles of the flexibility asset. In a real-life implementation this step can be skipped.
* The various baselines are determined (a) and evaluated (b), using the root mean square error (RMSE, Equation (1)) and mean absolute error (MAE, Equation (2)) as error metrics, where \(y_{j}\) is the reference profile and \(\hat{y}_{j}\) the baseline profile. \[RMSE=\sqrt{\frac{1}{n}\sum_{j=1}^{n}\left(y_{j}-\hat{y}_{j}\right)^{2}}\] (1) \[MAE=\frac{1}{n}\sum_{j=1}^{n}\left|y_{j}-\hat{y}_{j}\right|\] (2)
## 6 Simulation Setup
### Performance Metrics  
In the performance evaluation process of the AFB-GPSR protocol, and for comparison purposes, the following performance metrics are measured [52; 55]: packet-delivery ratio (PDR), end-to-end delay (E2 ED), network throughput and normalized routing load (NRL) [58].  
1. PDR: This refers to the ratio of packets successfully received by the destination to the total number of packets delivered from the source; \[PDR=(successfully\ received\ packets)/(delivered\ packets)\] (5)
2. E2 ED: This is the period of time occupied by a packet traveling from the source until it is successfully received by the destination; \[E2 ED=Packet\ Received\ time-Packet\ Delivered\ Time\] (6)
3. Throughput (bps): This is expressed as the number of bits received successfully by the destination over a specified time period. In the case of the GPSR routing protocol, control overhead includes the total number of transmitted beacon packets.  
Figure 8: The OMNeT++ Project Architecture to build the AFB-GPSR routing protocol.  
Figure 7: The simulation system configuration to implement the AFB-GPSR Protocol.  
\[Throughput=(Total\;Received\;Bits)\,/(Simulation\;Time) \tag{7}\]
4. Normalized Routing Load (NRL): It denotes the percentage of all routing control packets sent by all nodes divided by the number of data packets received at the destination: \[NRL=(Total\;Sent\;Control\;Packets)\,/\,(Total\;Received\;Data\;Packet)\] (8)
# Recent Advances and Challenges in the Seismo-Electromagnetic Study: A Brief Review
###### Abstract  
Due to their potential application in earthquake forecasting, seismo-electromagnetic phenomena were intensively studied for several decades all over the world. At present, measurements from ground to space have accumulated a large amount of observation data, proving an excellent opportunity for seismo-electromagnetic study. Using a variety of analytical methods to examine past earthquake events, many electromagnetic changes associated with earthquakes have been independently reported, supporting the existence of pre-earthquake anomalies. This study aimed to give a brief review of the seismo-electromagnetic studies preceding earthquakes and to discuss possible ways for the application of seismo-electromagnetic signals at the current stage. In general, seismo-electromagnetic signals can be classified into electric and magnetic changes in the lithosphere and perturbations in the atmosphere. We start with seismo-electromagnetic research in the lithosphere, and then we review the studies in the lower atmosphere and upper atmosphere, including some latest topics that aroused intense scholarly interest. The potential mechanisms of seismo-electromagnetic phenomena are also discussed. It was found that although a number of statistical tests show that electromagnetic anomalies may contain predictive information for major earthquakes, with probability gains of approximately 2-6, it is still difficult to make use of seismo-electromagnetic signals efficiently in practice. To address this, finally, we put forward some preliminary ideas about how to apply the seismo-electromagnetic information in earthquake forecasting.  
seismo-electromagnetic phenomena; precursors; earthquake forecast 2022
## 5 Triple-Frequency Medium RTK Performance Analysis
### Convergence Performance
Therefore, to increase the number of available BDS satellites, we conducted dual-frequency processing with B1I/B3I for the baseline HZDQ-HZLA and WHJ\(\times\)WHJA, as depicted in Figure 12. The fixing rate of the baseline HZDQ-HZLA and WHJ\(\times\)WHJA have increased by 1.3% and 0.9%, respectively, but they still significantly lag behind Scheme 2 and Scheme 3.
# FlexBFT: A Flexible and Effective Optimistic Asynchronous BFT Protocol
## 6 Discussion  
FlexBFT is an optimistic asynchronous BFT protocol designed to balance the efficiency of synchronous and semisynchronous BFT with the liveness of asynchronous BFT. This paper compares it experimentally with the semisynchronous protocol HotStuff, the asynchronous protocol Dumbo, and the state-of-the-art (SOTA) optimistic asynchronous BFT protocol BDT, demonstrating its superior performance.  
The primary contribution of FlexBFT is the concept of persisting in the slow lane. Unlike previous works that follow a linear, fixed execution path, FlexBFT can remain in the slow lane, or the back-up mode mentioned earlier, when network conditions are persistently poor. This approach eliminates the need to constantly fail in the fast lane and then switch to the slow lane through complex asynchronous protocols, such as ABA, before running the ACS protocol, thereby saving the overhead associated with fast lane timeouts and slow lane transitions.  
Within this new framework of persisting in the slow lane, determining when to return to the fast lane and how to optimize performance in the slow lane poses another challenge. Thus, FlexBFT's back-up mode does not solely rely on the ACS protocol but concurrently operates an optimistic path composed of semisynchronous consensus and a pessimistic path formed through MVBA. These paths are activated simultaneously during back-up mode operation, with the optimistic path speculatively outputting to reduce overall latency and probe whether the network conditions are sufficient for fast mode to operate normally.  
To further enhance performance, FlexBFT's fast mode, or fast lane, decouples semisynchronous consensus achieved through AVID into separate phases of consensus and downloading. This strategy makes fuller use of nodes' computational and bandwidth resources, thereby increasing the protocol's parallelism.  
The experimental results of this paper first validate that FlexBFT, as an optimistic asynchronous BFT protocol, indeed manages to combine the advantages of both HotStuff and Dumbo across different environments, showing commendable performance in throughput, latency, and liveness. Second, the experimental results concerning the latency-throughput trade-off, along with those under conditions of increased network volatility, highlight FlexBFT's advantages over BDT.
# Joint Trajectory and Communication Design for Buffer-Aided Multi-UAV Relaying Networks
###### Abstract  
With the rapid development and evolvement of unmanned aerial vehicle (UAV) technology, UAV aided wireless communication technology has been widely studied recently. In this paper, a buffer aided multi-UAV relaying network is investigated to assist blocked ground communication. According to the mobility and implementation flexibility of UAV relays, it is assumed that the communication link between air-to-ground is the Rician fading channel. On the basis of information causality, we derive the state change of the information in the buffer of UAV relays and maximize the end-to-end average throughput by join the relay selection, UAV transmit power, and UAV trajectory optimization. However, the considered problem is a mixed integer non-convex optimization problem, and therefore, it is difficult to solve directly with general optimization methods. In order to make the problem tractable, an efficient iterative algorithm based on the block coordinate descent and the successive convex optimization techniques is proposed. The convergence of the proposed algorithm will be verified analytically at the end of this paper. The simulation results show that by alternately optimizing the relay selection, UAV transmit power, and UAV trajectory, the proposed algorithm is able to achieve convergence quickly and significantly improve the average throughput, as compared to other benchmark schemes.  
U
# Enhancement of Low-Light Images Using Illumination Estimate and Local Steering Kernel
## 3 Proposed Method
### Illumination Estimate
To address these issues, we further extend the dynamic range for pixel values in low-light areas to naturally disperse concentrated pixel values in bright levels. The proposed algorithm extends the dynamic range by classifying the cases where the pixel values in the enhanced image are smaller than the median of the maximum pixel values that the image can represent as low-light regions, as shown in Equation (13). \[S_{p}(x,y)=\left\{ \begin{array}{rl}\hat{S}(x,y),&if\ 0.5<\hat{S}(x,y)\\ \\ 2\hat{S}(x,y)^{2},&otherwise\end{array} \right. \tag{13}\]
## 5 Discussion
### Effect of Groundwater seepage on Frost Heaving Displacement of Ground  
In this section, taking the tunnel buried depth of 15 m as an example, five groundwater seepage velocities (0 m/d, 0.5 m/d, 1.0 m/d, 1.5 m/d and 2.0 m/d) are considered to explore the distribution law of stratum frost heaving displacement under the condition of groundwater seepage.  
The development law of vertical displacement of the strata under different seepage velocities is shown in Figure 14. The vertical freezing expansion of the stratum is distributed asymmetrically along the center of the tunnel, which is due to the transfer of cold from the upstream freezing pipe to the downstream stratum under the action of seepage flow, resulting in the uneven thickness of the upstream and downstream freezing walls and the offset of the peak position of the stratum in the upper part of the stratum tunnel. When the effective frozen wall thickness is reached, the maximum vertical displacement of the upper stratum of the tunnel under different seepage velocity (0 m/d, 0.5 m/d, 1.0 m/d, 1.5 m/d and 2.0 m/d) is 43.1 mm, 45.2 mm, 48.2 mm, 51.0 mm and 53.5 mm.  
Figure 14: Distribution of stratum vertical displacement field of stratum under different seepage velocities: (**a**) 0 m/d; (**b**) 0.5 m/d; (**c**) 1.0 m/d (**d**) 1.5 m/d; and (**e**) 2.0 m/d.  
The surface frost heaving displacement curve is shown in Figure 15, which is \"normal distribution\"; furthermore, the peak of the single peak locates directly above the center of the tunnel when the seepage velocity is 0 m/d, and the position of the peak will \"shift\" to the direction of water flow with the increase in seepage velocity.  
The maximum frost heaving displacement is located above the center of the tunnel under no seepage condition. When the flow velocity reaches 0.5 m/d, it is located 161 mm downstream from the center of the tunnel. When the flow velocity reaches 1.0 m/d, it is located 374 mm downstream from the center of the tunnel. When the flow velocity reaches 1.5 m/d, it is located 556 mm downstream from the center of the tunnel, and when the flow velocity reaches 2.0 m/d, it is located 968 mm downstream from the center of the tunnel. These data show that the existence of seepage leads to the thickness of frozen wall downstream being greater than that upstream, which leads to the deviation of the maximum frost heaving positive on the ground.
## 2 Methodology
### Step 2: Energy Efficiency Model  
An approach based on Random Forest (RF) algorithm was used for predictions. This machine learning algorithm, based on the combination of decision trees, consists of obtaining a segmentation of data by empirical means, applying a consecutive series of simple rules (tree). RF does not require excessive computational capacity while it is able to fit complex nonlinear relationships [12; 16; 36].  
RF, developed by [PERSON] [37], has been highly successfully applied in general problems both as a regression and classification method, being versatile enough to be applied to problems Large scale and easily adapt to various ad hoc learning tasks [38]. This approach combines several random decision trees using perturbation and combination techniques [39] that consist of creating a set of diverse classifiers introducing randomness in the process of construction of said classifiers. The prediction of the considered set is obtained by using the average prediction of the individual classifiers [40].  
In this study, the dataset was firstly tested as a whole and in a second experiment split into 18 groups based on the socioeconomical variables, the ACORN categories, as it was understood that they behave differently.  
The available data were divided in a 75:25 test:training ratio based on energy sequential observations for each ACORN group (see top left boxes in Figure 2). Thus, the first 613 days of a total of 818 observations of each group were considered for training and the last 205 days for testing. This selection gives a better approximation for a real application, forecasting the energy demand in the next N days after a giving date and under certain conditions, allowing the study of possible deviations that the forecast could suffer over time and the implications this could have in the range of algorithms applicability.  
In RF, the more trees assembled, the greater the precision and accuracy, although the results cease to improve significantly beyond a critical number of trees, causing over-fitting. To prevent this phenomenon and ensure the optimal maximum depth of trees, a cross-validation technique was implemented before modeling (see phase number 1 in Figure 2 for more detail), using the training set, 10 random folds and 20, 22, 24, 26, 28 and 30 depths, the maximum number of trees in Spark. With respect to the prediction parameters, the models themselves are able to select the most appropriate ones based on their correlation strength with the explanatory variable and avoid those autocorrelated.
## 2 Outdoor Positioning in the IoT Ecosystem
### Location-Enabled IoT Technologies
#### 2.3.2 Sigfox  
Sigfox is a wireless technology proposed for long-range and low-power communication for IoT systems. Sigfox works in a license-free spectrum, and it provides network coverage in different countries by collaboration with different companies. Therefore, Sigfox does not allow private networks. This subsection discusses different layers and features of Sigfox. Sigfox provides unidirectional and bidirectional communication. It works in the license-free channel of 868 MHz (in Europe), 902 MHz (in the US), and 433 MHz (in Asia). Sigfox adopts Ultra Narrow Band (UNB) radio transmission for both uplink and downlink communication [26]. UNB provides long-range communication by passing through solid objects. The uplink bandwidth of Sigfox is 100 Hz (in Europe) and 600 Hz (in the US). The downlink bandwidth of Sigfox is 1.5 kHz. Sigfox uses Differential Binary Phase-Shift Keying (DBPSK) modulation for uplink transmission and Gaussian Frequency-Shift Keying (GFSK) modulation for downlink transmission. DBPSK has more efficiency in the spectrum medium access than GFSK that leads to uplink range increment. By doing this, the lower transmit power in the uplink band is compensated [27].  
Similar to LoRaWAN, Sigfox has star topology and asynchronous communication [28]. Asynchronous communication allows the end nodes to be in sleep time most of the time. Sigfox Architecture is similar to LoRaWAN. End nodes are connected to base stations, and they can communicate with the Sigfox network server throw base stations. Connection to several base stations leads to handover protocol needless. Sigfox network server connect to base stations throw IP/UDP network. Sigfox network server collects data from base stations, and it is responsible for network control and end nods authority.  
Sigfox MAC employs slotted ALOHA protocol and provides two types of communication between an end node and base station: unidirectional and bidirectional transaction. In the unidirectional transaction, the end-node transmits several uplink frames in a randomly selected frequency channel and at different time intervals. This leads to resilience in the presence of issues such as interference and multipath fading. In a unidirectional transaction, the Sigfox network server cannot send downlink data to the end nodes. In a bidirectional transaction, after the end node sends uplink data same as a unidirectional transaction, the Sigfox network server sends downlink data to the end node.
# CPM-OFDM Performance over Underwater Acoustic Channels
## 2 Related Work
Authors have used a maximum ratio combining (MRC) receiver and the results show that the proposed scheme is 3 dB superior to CPM-OFDM. BER performance of constant envelope multicarrier modulation was evaluated in [33]. In this work, instead of using IFFT, authors have employed discrete cosine transform (DCT). The justifications given by the authors for using DCT were, the capability of DCT to process time and frequency domain data using real numbers, and bandwidth efficiency of DCT over DFT as the former requires half the bandwidth when compared with the latter. A thorough literature survey suggests that no work has been done to date that evaluates the performance of CPM-OFDM over underwater acoustic channels. Hence, the proposed work is the first of its kind and significantly contributes to the body of knowledge.
## 4 The Physical Layer Security Performance of the Relay Transmission Model
### The Influence of the Relay Power for P(Cs > 0)
#### 4.1.2 Experimental Results with Various Relay Power
Moreover, in malicious relay behavior case, \(P(C_{S}>0)\) increases when \(P_{r}/P_{s}\) is beyond 5 dB, which is different to theoretical simulation result. It indicates that when the malicious relay forwards noise power high enough, the eavesdropper channel will be deteriorated more heavily than the main channel. As for the influence of relay power, it can be found that there exists a maximum value of \(P(C_{S}>0)\) for different relay powers.