esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Opportunities and Future Challenges
#### 5.1.1 Opportunities  
The biggest opportunity of IoT technologies is related with the creation of smart and cognitive buildings, where it will be possible to manage the human activity in indoor environments, identifying new opportunities to save and improve the occupant's experience. IoT devices can be used to increase buildings' energy efficiency, thermal comfort, and IAQ as they not only monitor many parameters but can also act physically into spaces. Just to cite a few, these devices can be used for: (i) Collecting indoor data on which devices or operations have the highest energy consumption; (ii) in which period do they use more energy; (iii) how do indoor and outdoor activities affect IAQ; (iv) what are their impacts on the productivity and health of the occupants. Part of these IoT devices can switch autonomously any equipment on and off (even temporarily), for instance, when checking for work inactivity, unadjusted temperatures, or high levels of atmospheric pollutants. Extrapolating this resource management to larger areas, such as entire buildings, using programmable and interconnected IoT switches, it becomes possible to optimize and balance the total resources distribution.  
On the other hand, by streaming data from all these IoT-devices to the Cloud, it will be possible to predict future patterns, determining how energy consumption or IAQ patterns will be in the short/midterm. One idea that stands out from the examined works is the prediction of indoor pollution levels even before reaching the threshold levels and acting in a preventive way. So, another opportunity will be the inclusion of autonomous airflow control systems and prediction models/algorithms combined with other measured variables for energy efficiency, thermal comfort, noise pollution, etc. Machine learning and artificial intelligence also work very well with IoT devices and can be a big help in decision-making and preventing strategies.  
A major save-energy measure is the development of dashboard control and alert applications for smartphone or tablet use. This can make work easier, as these devices bring mobility in the administration of resources, which can be done anytime and anywhere.  
IoT-based systems are extremely versatile and easily adapt to the needs of any type of building or business activity (large or small), offer scalability, and improved security when well implemented. Tasks generally neglected by occupants, such as turning off the lights and equipment, or opening and closing windows to ventiate spaces, will always be fulfilled. So, another opportunity will be to provide systems with capabilities to inform in good time for the need for preventive and corrective actions in the various installed systems and equipment, avoiding downtimes in the control of smart building resources.
# Bayesian Filtering Multi-Baseline Phase Unwrapping Method Based on a Two-Stage Programming Approach
## 3 Bayesian Filtering MBPU Method  
The Bayesian filtering method has been widely used in various fields. The first step of the Bayesian filtering method is to obtain the corresponding state equation and observation equation, according to the specific problem. Then, the state value and the variance of the state equation are predicted, according to the state equation. Finally, we update the data according to the observation equation. As the Bayesian filtering method involves obtaining the unwrapped phase through the state estimation method, not a simple phase gradient summation method, it has also been widely used in PU. To address the MBPU problem, we can obtain the corresponding state equation and observation equation as follows:  
\[\left\{\begin{array}{ll}\psi_{i}(k)&=\psi_{i}(k-1)+\delta_{i}(k-1)+\omega_{i }(k-1)\\ &=f_{i}[\psi_{i}(k-1)]+\omega_{i}(k-1)\\ \phi_{i}(k)&=\left\{\begin{array}{l}\sin(\psi_{i}(k))\\ \cos(\psi_{i}(k))\end{array}\right\}+\left\{\begin{array}{l}v_{i_{1}}(k)\\ v_{i_{2}}(k)\end{array}\right\}=h_{i}[\psi_{i}(k)]+v_{i}(k)\end{array}\right. \tag{10}\]where \(\psi_{i}(k-1)\) represents the true unwrapped; \(\delta_{i}(k-1)\) and \(\omega_{i}(k-1)\) represent the true phase gradient and its estimation error;\(f_{i}[\psi_{i}(k-1)]\) represents the evolution model; \(h_{i}[\psi_{i}(k)]\) represents the ideal observation vector; and \(v_{i}(k)\) represents the observation variances. Equation (10) is the state equation and observation equation of the Bayesian filtering MBPU method, upon which we base the Bayesian filtering MBPU framework proposed in this paper. This paper will propose several MBPU models of Bayesian filtering.
# Channel Access in Wireless Smart Grid Networks Operating under ETSI Frame-Based Equipment Rules
## 3 Channel Access
### FBE Variants
#### 3.2.5 Enhanced FBE  
[PERSON] and [PERSON] [35; 39] define a method to improve fairness in channel access for LTE unlicensed (LTE-U) networks coexisting with Wi-Fi. Since LTE-U channel access shares some common features with FBE (most notably a duty cycle approach), we adapt this scheme and assess its performance in the following, referring to it as enhanced FBE (E-FBE). E-FBE introduces a backoff mechanism before channel access and defines two types of CCA: initial CCA (ICCA) and extended CCA (ECCA). ECCA is responsible for decrementing backoff after detecting an idle channel. On the other hand, during ICCA, backoff cannot be decremented even if this operation is successful. If an E-FBE node wants to access the channel, it will perform ICCA. If ICCA is successful, the node selects a random number \(N\) from the range \([0,Q]\), where \(Q\) defines the maximum backoff value. Then, the node enters the backoff phase, which starts with repeated ECCAs performed until \(N=0\). If a transmission occurs during ECCA or ICCA, the node has to become silent for the COT, wait for ICCA at the end of the FFP, and perform a new backoff value selection. When the node reaches \(N=0\), it immediately starts transmission. In Figure 6, an example of the backoff in E-FBE can be found. At the beginning, the node successfully completes ICCA and the first ECCA. During the second ECCA the channel is sensed as busy and the node is silent until the end of the FFP. Finally, the whole backoff phase is successfully performed and the channel is accessed.  
Figure 5: Flowchart of the DB FBE algorithm.  
Figure 6: Operation of enhanced FBE.
# Systematic Review: Anti-Forensic Computer Techniques
## 4 Results
### Weaknesses and Anti-Forensic Use Cases
#### 4.1.8. Weaknesses and Anti-Forensic Use Cases in Mobile Device Operating Systems  
These studies include those applied to the case of S.S.O.O. of mobile devices of any kind, i.e., phones, tablets, smartwatches, televisions, etc. The O.S.S. treated by the authors refers to two in particular: Android and IOS.  
Some articles focus on exposing techniques that make it difficult to gather forensic information on Android, such as that of [PERSON] et al. (2019); [PERSON] et al. (2019).  
[PERSON] et al. (2020) describe a tool designed to hinder the forensic analysis of iOS mobile devices (iPhone and iPad).  
This category also includes works on O.S. applications for mobile devices, such as the work by [PERSON] et al. (2019) on anti-forensic techniques dedicated to the WhatsApp messaging application.  
[PERSON] et al. (2020) relate anti-forensic techniques applied to mobile devices, presenting some automated instances of such techniques to Android devices, testing the effectiveness of such techniques against a cursory examination of the device and some acquisition tools.  
[PERSON] et al. (2020) present an anti-forensic approach for Android devices that protects AES keys from being acquired by forensic tools.  
Further articles explore techniques such as data deletion and manipulation to evade existing forensic tools ([PERSON] et al., 2020), operating system modifications to block data extraction ([PERSON] et al., 2019; [PERSON] et al., 2019), and the exploitation of vulnerabilities in applications such as WhatsApp ([PERSON] et al., 2020). The articles in question also highlight the need to develop more robust forensic tools and methodsto counter anti-forensic techniques, crucial for criminal investigation and the protection of digital evidence on mobile devices. Some articles propose solutions such as disabling vulnerable services on iOS [175] and protecting encryption keys on Android [178].  
As novel contributions, several articles [173; 174; 177] demonstrate the need to improve forensic tools for mobile devices. Article [175] analyses the vulnerabilities of specific forensic services on iOS. Article [176] explores WhatsApp vulnerabilities for anti-forensic purposes. Finally, article [178] proposes a novel method of protecting encryption keys on Android.
## 4 Phase Noise and Phase Error Analysis  
The phase noise, which are the important performance parameters of an oscillator, can be calculated by the method outlined in Reference [35] and can be represented in the block diagram of Figure 4. The spectral density of the phase noise is defined in Reference [35].  
\[\left|\frac{Y}{X}[j(\omega_{o}+\Delta\omega)\right|^{2}=\frac{1}{(\Delta \omega)^{2}\left|\frac{dH}{d\omega}\right|^{2}} \tag{14}\]  
where \(\omega_{o}\) is oscillation frequency and \(\omega=\omega_{o}+\Delta\omega\) is carrier frequency approximation.  
If \(H(j\omega)=A(\omega)\exp[j\phi(\omega)]\), then:  
\[\frac{dH}{d\omega}=(\frac{dA}{d\omega}+jA\frac{d\phi}{d\omega})\exp[j\phi( \omega)] \tag{15}\]  
Substitute Equation (15) into Equation (14) and get:  
\[\left|\frac{Y}{X}[j(\omega_{o}+\Delta\omega)\right|^{2}=\frac{1}{(\Delta \omega)^{2}[(\frac{dA}{d\omega})^{2}+(\frac{d\phi}{d\omega})^{2}]}\text{ for }\omega\approx\omega_{o}\text{, }A\approx 1 \tag{16}\]  
The phase noise of the proposed circuit can be obtained by Equations (14)-(16).  
Quadrature oscillators are widely used in communication systems due to its in-phase and quadrature phase signals, which deviate from \(90^{\circ}\). Therefore, the quadrature phase accuracy between the two output signals should be reasonable. Generally, the in-phase and quadrature phase expressions of the quadrature oscillator signals can be written as:  
\[V_{I}(t)=V_{m}(I)\cos(\omega t+\theta(I)) \tag{17}\]  
\[V_{Q}(t)=V_{m}(Q)\cos(\omega t+\theta(Q)) \tag{18}\]  
\[\theta(Q)-\theta(I)=\frac{\pi}{2}+\phi \tag{19}\]  
Figure 4: Linear oscillatory system in Reference [35].  
where \(\omega\) is the oscillation frequency and \(\phi\) is the phase deviation from the quadrature signal. \(V_{I}(t)\) is the in-phase signal and \(V_{Q}(t)\) is the quadrature phase signal. The phase error of the proposed circuit can be obtained by Equations (17)-(19).
## 2 Materials and Methods  
### Study Design  
This is an observational, case control study carried out in our PD center at San Bortolo Hospital (Vicenza-Italy) between 1 January 2020 and 31 December 2020. The case group consisted of all eligible patients who came to the Vicenza center with signs and symptoms suggestive of peritonitis in the period study. The inclusion criteria were an age over 18 years, PD treatment for at least 30 days, and informed consent. We excluded patientswith relapsing and recurrent peritonitis. The control group consisted of a similar number of patients who were scheduled for routine visits at our PD center during the same period. The inclusion criteria for the control group were age over 18 years, PD treatment for at least 30 days, and informed consent. The exclusion criteria were the presence of a current peritonitis episode or the presence of previous peritonitis or any history of systemic inflammation 30 days before enrollment. All patients were provided written informed consent, and the study was conducted in accordance with the principles of the Helsinki Declaration and Good Clinical Practices.  
Based on the ISPD criteria, peritonitis is defined as a cloudy peritoneal dialysate effluent (PDE) with a white cell count \(>\) 100 \(\times\) 10\({}^{6}\)/L (after a dwell time of at least 2 h), of which more than 50% were polymorphonuclear cells, typically with abdominal pain and/or a positive effluent culture [10]. All patients underwent blood and PDE analysis according to our clinical practice on the first day of peritonitis for the case group and during routine checkout visits for the control group. Medical features, laboratory parameters, and dialysis-related data were collected for all subjects at the time of enrollment. Blood urea nitrogen, serum creatinine (sCr), C-reactive protein (CRP), and other biochemistry parameters were detected by the usual laboratory techniques performed by an automatic analyzer (Dimension Vista, Siemens Healthcare, Tarrytown, NY.) White blood cells (WBC) and hemoglobin (Hb) were evaluated by the automated hematology analyzers XN 9000 (SYSMEX, KOBE, Japan). Weekly Kt/Vurea and Creatinine Clearance (wCCr) were applied as estimates of PD adequacy. Weekly Kt/Vurea and wCCr were estimated based on the 24 h drain volumes carried out before the planned visit to the PD center [12]. The white cell count in PDE was assessed by the collection of PD effluent in all patients by the automated hematology analyzers XN 9000 (SYSMEX, KOBE, Japan). PDE culture and gram staining were performed in the case group.
## 2 Methods
### Landing Strategy and MAV Control  
The proposed landing procedure is performed as follows:  
STEP 1.The MAV searches the spherical object and yaws until the object is located at the center of the image captured by camera 1.  
STEP 2.The MAV approaches the platform while adjusting its position to keep the spherical object at the center of the image in STEP 1.  
STEP 3.When the MAV is sufficiently close to the platform, it begins to descend in search of the align marker.  
STEP 4.The descending motion stops when the alignment marker is at the center of the image.  
STEP 5.The MAV rotates around the platform according to the color of the align marker.  
STEP 6.Camera 2 detects the marker under the MAV and acknowledges its position.  
STEP 7.The MAV is guided to the target location on the platform and lands.  
When the MAV approaches the platform (STEP 2), the radius of the spherical object in the image is detected and is used to estimate the distance between the MAV and the platform. The align marker used in STEP 5 is a cylindrically shaped object; three different-colored markers are located on its surface. Thus, depending on the detected marker color, the MAV determines the direction of rotation. To improve the accuracy of the alignment, the marker is designed as follows: when the MAV is perfectly aligned with the platform, only the center marker is captured, and the other two markers are invisible. To this end, thin walls are attached around the center marker, as shown in Figure 1c. This design enables the MAV to detect only the center marker when it is aligned. However, the shadow of the thin wall falls on the center marker. Thus, a light-emitting diode (LED) was installed  
Figure 1: MAV and landing system. (**a**) MAV with camera and bottom marker; (**b**) landing platform with a spherical object, align marker, and camera; (**c**) align marker.  
near the third marker to prevent detection failure. For the detection of the align marker and the marker attached to the bottom of the MAV (STEP 5 and 6), HSV filter-based detection was used. Figure 2 illustrates the overall landing procedure.  
The MAV is controlled by seven velocity commands (forward, backward, left, right, up, down, and yaw) for maneuvering. Note that the MAV was controlled using the position data (in pixels) from the camera images. For example, when the MAV approaches the platform (STEP 2), the position of the spherical object (in pixels) in the image is selected as the reference position. A proportional-derivative (PD) control was used to allow a swift flight control of the MAV, as shown in Figure 3. Thus, the velocity command \(u(t)\) can be obtained as  
\[u(t)=K_{p}e(t)+K_{d}\bigg{(}\frac{e(t)-e(t-\Delta t)}{\Delta t}\bigg{)}, \tag{1}\]  
Figure 2: Proposed autonomous landing procedure.  
where \(e(t)\) is the pixel position error at time \(t\), and \(\Delta t\) is the time interval. \(K_{p}\) and \(K_{d}\) are the coefficients for the PD control. The values of \(K_{p}\) and \(K_{d}\) for SETP 2 were determined as 0.2 and 0.09, respectively, and 0.13 and 0.075 for STEP 7.
# Building Survivable Software Systems by Automatically Adapting to Sensor Changes
## 1 Introduction
To recover the original sensor values of the temperature sensor, we use a reconstruction function \(f\) by leveraging sensor values from the remaining sensors and the new sensors. One approach to learning such a reconstruction function is to simply ignore the new sensors and reconstruct the replaced sensors using the remaining ones. Assuming that we have access to sufficient historical sensor values of these sensors, such a reconstruction function can be learned straightforwardly via classical regression methods [13]. In such methods, the sensor values of the remaining sensors are treated as the input variables, and the sensor values of the replaced sensors are treated as the output variables. However, the new sensors may contain complementary information over the remaining sensors, which may help us better reconstruct the replaced sensors. As an extreme example, if the new sensors are exactly the same as the replaced sensors, using their sensor values definitely aids reconstruction. Learning a reconstruction function that exploits new sensors poses unique challenges since there is no overlapping period of time between the replaced and new sensors. If we intend to apply classical regression methods to learn the reconstruction function, we need  
Figure 1: Example of a compound weather sensor that consists of three individual sensors. The reconstruction function \(f\) reconstructs failed temperature values from the two remaining working sensors (blue arrows) and the two new sensors (red arrows). training samples that contain the sensor values of both the replaced and the new sensors at the same timestamps. However, since there is no such overlapping period of time, the required training samples are not available and classical regression methods are therefore inapplicable. To address this challenge, we propose an approach called ASC (Adaptation to Sensor Changes) that learns a reconstruction function to preserve sensor value distributions before and after the sensor change. We further improve ASC in two aspects motivated by real-world applications. First, we propose a method to dynamically estimate the adaptation quality, which enables higher-layer software components to determine whether or not to accept an adaptation. Second, we develop a procedure to deal with a large number of sensors by selecting a subset of important sensors. This procedure can significantly reduce the overfitting to noisy values as well as the overall computational cost. It enables our approach to continually exploit new sensors in an open environment. For empirical studies, we evaluate ASC on sensor data from the weather and UUV domains. In most of the evaluation cases, ASC outperforms other baseline approaches, achieving an average improvement of 5.7%. Our work described in this paper appears in the first author's Ph.D. dissertation [14].
# A Survey of Visible-Light-Communication-Based Indoor Positioning Systems
## 6 Performance Evaluation and Benchmarking
### Research Challenges and Future Work
#### 6.2.2 Ease of Use and Robustness  
VLP systems have significant potential for widespread adoption in everyday devices if they can be easily implemented and designed to be user-friendly. However, many existing approaches have only been demonstrated in specific scenarios. For example, the positioning accuracy of the system in [73] was primarily tested in dark environments. Furthermore, experimental results and performance evaluations are often conducted in simplistic laboratory settings, such as unobstructed environments, which do not accurately reflect the complexity of living spaces. It is also important to note that some systems require a preliminary training phase prior to deployment, requiring additional preparation time and effort [73; 79]. This training phase can be particularly resource-intensive when adapting to new or modified environments.  
Evaluating performance in different work environments is critical to ensuring the robustness of positioning systems [14]. Examples of such environments include large conference rooms, small cubicles, and conventional corridors. It is important to recognize that variations in the number of available transmitters can significantly affect the positioning accuracy and continuity of service. Furthermore, accommodating user mobility is essential for the practical application of these systems [41]. In this context, the design of the receiver plays a key role in providing highly accurate positioning information to user applications.
## 3 Denoising Diffusion Probabilistic Model (DDPM)
### Advantages of Using DDPM in Biomedical Image Segmentation  
The application of DDPM in biomedical image segmentation offers several advantages that contribute to improved accuracy, robustness, and reliability in delineating structures within medical images.
## 5 Review of Case Studies: Flying Vehicle Routing Optimization
Future work will focus on optimizing refueling management and developing a formula to determine the refueling time based on UAV energy requirements, aiming to further enhance the efficiency and performance of UAV-based pickup and delivery systems. In a study conducted by [PERSON] et al. [9], a model for energy consumption in drone delivery was developed. They proposed a cost function that takes into account drone reuse, aiming to identify sub-optimal solutions in real-life scenarios. To optimize the overall cost and delivery time, an MILP formulation was used, and the problem was solved using the simulated annealing (SA) heuristic. The research findings demonstrated that, by optimizing battery weight and promoting drone reuse, significant improvements of over 10% could be achieved compared to scenarios where each drone had an identical battery weight. [PERSON] et al. [52] presented a nonlinear MIP formulation that aimed to optimize homogeneous drone operations by minimizing costs while considering battery wear and disposal effects. To solve this problem, they implemented a tailored branch-and-price algorithm, which demonstrated practical applicability by successfully solving instances with up to 100 customers within the given time limit. In a similar vein, [PERSON] et al. [74] proposed a three-stage stochastic IP model that accounted for the uncertainty of takeoff and breakdown conditions in the context of minimizing the total delivery costs while adhering to traveling distance limitations. To handle the high complexity of this optimization problem, they adopted an L-shape decomposition method, which enabled an efficient solution computation. Table 3 provides a summary of the key parameters and contributions of each paper, allowing for a comparative analysis of the problem addressed, objectives, optimization approach, and key contributions of each study.