esa-sceva/satcom-chunk-collection huggingface dataset:
# On Developing Techniques for Sharing Satellite Spectrum with Indoor Small Cells in 5G +
## 3 Mathematical Analysis
### Estimation for a Single Building
#### 3.1.3 When Only a SBS is Deployed per Apartment  
If only a SBS exists within an apartment, the satellite spectrum can be shared with the SBS without employing the ABS based eICIC technique due to the nonexistence of a CGC station in the apartment. Hence, the aggregate capacity of the small cell UEs for any SBS (i.e., \(\sigma_{\text{s}}:s\in\left\{1,2,\ldots,S_{\text{F}}\right\}\)), when only a SBS exists per apartment in \(t\)\(\in\)\(\mathbf{T}\) is given by  
\[\sigma_{\text{s,sb}}=\sum_{t\in\mathbf{T}}\sum\
olimits_{i=1}^{M_{\text{SPS}}} \sum\
olimits_{u=1}^{U_{\text{F,max}}}\sigma_{t,i,u}\Big{(}\rho_{t,i,u}\Big{)} \tag{7}\]  
where \(\sigma\) and \(\rho\) are responses over \(M_{\text{SPS}}\) RBs of \(u\in\left\{1,2,\ldots,U_{\text{F,max}}\right\}\) UEs for any SBS. If each SBS serves the maximum of one UE (i.e., \(U_{\text{FU,max}}=1\)), then Equation (4) is given by  
\[\sigma_{\text{s,sb,1}}=\sum_{t\in\mathbf{T}}\sum\
olimits_{i=1}^{M_{\text{SPS}}} \sigma_{t,i}\Big{(}\rho_{t,i}\Big{)} \tag{8}\]  
If each SBS has exactly one small cell UE, and all SBSs are serving simultaneously, the aggregate capacity per 3D building is then given by  
\[\sigma_{\text{cl,sc}}^{\text{SBS only}}=\sum\
olimits_{s=1}^{S_{\text{F}}} \sigma_{\text{s,sb,1}} \tag{9}\]
# A Comprehensive Review of Sensor-Based Smart Building Monitoring and Data Gathering Techniques
## 5 Data Aggregation Overview
### Real-Time Analytics  
Within the domain of smart buildings, heterogeneous sensors [53] generate substantial volumes of data at an accelerated rate, stemming from the persistent monitoring of the physical environment [54]. Consequently, the implementation of real-time analytics (RTA) becomes imperative for the continuous and seamless processing of data streams. RTA is a technique for real-time data processing and analysis, offering valuable insights and enabling prompt decision making. Real-time analytics of IoT data can offer valuable information for decision making within IoT systems, thereby improving both system efficiency and reliability in a timely manner [55].  
Unlike traditional batch processing, which analyzes data at set intervals, RTA offers an immediate and continuous understanding of events and data as they arrive within a specified timeframe. This time interval is usually measured in milliseconds, microseconds, or even nanoseconds, depending on the system. However, many applications might necessitate a combination of batch and real-time data processing (e.g., Lambda architecture). RTA is based on stream processing, which primarily involves ingesting a continuous stream of events to filter, enrich, join, transform, convert, route, and analyze data as they arrive in near-real-time [56; 57]. As described by [PERSON] et al. [57], a real-time property necessitates gathering data from multiple sources and processing them immediately upon arrival. This process frequently includes alerting humans to important events in the environment, triggering system components to execute specific tasks, or both.  
The essential features of an efficient RTA include: (1) low latency to ensure timely responses to events within set time limits, (2) high availability to immediately process incoming events and avoid difficulties in storing or buffering high-volume, high-velocity data streams, and (3) horizontal scalability, which allows for the dynamic addition of servers to handle varying data volumes or processing workloads, ensuring that data are processed within the required time frames.
# Design and Evaluation of Flooding-Based Location Service in Vehicular Ad Hoc Networks+
## 4 Performance Analysis
### Numerical Results  
In our numerical examples, densities \(\lambda\) of the nodes were chosen so that the mean number of neighbors of a node was 20; transmission range was \(R=100\).  
Figure 6 presents the mean number of broadcast messages per location update versus the maximal path lengths computed by the analytical model and simulations in the case of a homogeneous linear (1D) network. Analytical results are represented by a continuous line, and simulation results by dots and error bars. The same color is used for the same value of \(q\) for the simulations and the analytical model. We observed very good matching of the two approaches. The black curve shows the number of messages with the pure flooding algorithm, and it clearly shows the great benefit of SFLS.
## 4 Discussion
### Limitations of the Study  
Although the method used in this study has been improved and innovated to a certain extent compared to other methods, there is still some room for improvement. First of all, the study did not fully take into account the element of human activities, and social and economic data were not fully utilized, which led to the landscape elements close to cultivated land, construction land, and other areas to be underestimated in terms of their level of prioritization [10; 34]. Secondly, the study does not factor in the diverse climatic conditions and unbalanced human activities in Sichuan Province; these factors will affect the future changes in land use types and make the ESP constructed in this study have a certain deviation from the real situation [10].  
In addition, although the contribution of the urban ecosystem to all kinds of ESs is small, simply setting its value to 0 cannot reflect reality. [PERSON] et al. showed that fragmented urban green spaces have a positive impact on bird biodiversity [3], so it is also meaningful to quantify the ESV of construction land in the study. Limited by the accuracy of data acquisition, glaciers, wetlands, and other ecosystems are not fully reflected in this study. Although these ecosystems are less distributed in the study area, they still affect the research results to a certain extent.
# Whisper: Programmable and Flexible Control on Industrial IoT Networks
## 1 Introduction
When receiving such messages, receivers are persuaded to change their next-hop and manage the wireless resources accordingly. We demonstrate that Whisper works on already established routing and scheduling protocols designed for IoT networks without modifying them neither adding additional software in the devices. Namely, our implementation works on top of the Routing Protocol for Low-power and lossy networks (RPL) [4] and 6 TiSCH [5], an industrial IoT open standard for running IPv6 in the Timeslotted Channel Hopping (TSCH) mode of IEEE 802.15.4e (one vendor alone reports over 39,000 TSCH networks deployed, which is over 10-Billion operating hours [6]). While RPL are widely used, proprietary scheduling protocols may be run in industrial deployments instead of 6 TiSCH. We strongly believe that Whisper can be easily extended to RPL based proprietary technologies, subject to syntactic changes in the messages sent by the Whisper controller. We identify alternative message types, dissemination possibilities, and deployment options to implement Whisper centralized controllers. We design an algorithm for Whisper controllers to efficiently calculate the combination of those alternatives that reduces control overhead and device load the most. The result is that Whisper can operate by only deploying the controller at the RPL-tree root node. With only the controller in place, however, it is not always possible to arbitrarily control routing and scheduling at every node, even when technically possible (e.g., next-hops are in the wireless range of their respective previous hops). The Whisper-controller algorithm identifies those cases, indicates where to deploy new nodes (if needed), and optimizes their usage--e.g., to forward traffic between two consecutive hops on the desired path, or allow additional scheduling operations. We refer to such new nodes as Whisper nodes. In building Whisper, we make the following contributions:  
* We describe the basics of Whisper, and compelling use cases unlocked by our system (Section 3.1). * We detail the design of the Whisper controllers, along with their internal algorithms to translate high-level policies into a limited number of control messages (Sections 3.3 and 3.4). * We pinpoint the limitations of only relying on a single Whisper controller, and show how the controller can systematically use Whisper Nodes (if deployed or deployable) to circumvent those limitations (Section 4). * We report on our Whisper implementation (Section 5), and evaluate the effectiveness of our approach with simulations and experiments with real IoT devices (Section 6). * We outline Whisper longer-term perspectives (Section 7).
###### Abstract  
In this paper, the time-varying formation tracking problem of the general linear multi-agent system is discussed. A distributed formation tracking protocol based on Riccati inequalities with adaptive coupling weights among the follower agents and the leader agent is designed for a leader-following multi-agent system under fixed and switching topologies. The formation configuration involved in this paper is expressed as a bounded piecewise continuously differentiable vector function. The follower agents will achieve the desired formation tracking trajectory of the leader. In traditional static protocols, the coupling weights depend on the communication topology and is a constant. However, in this paper, the coupling weights are updated by the state errors among the neighboring agents. Moreover, the stability analysis of the MAS under switching topology is presented, and proves that the followers also could achieve pre-specified time-varying formation, if the communication graph is jointly connected. Two numerical simulations indicate the capabilities of the algorithms.  
time-varying formation; adaptive control; multi-agent system; switching interaction topologies; leader-following tracking +
Footnote †: journal:  
0021  
Distributed Adaptive Formation Tracking Control under Fixed and Switching Topologies: Application on General Linear Multi-Agent Systems.
# A New Method for Large-Scale Landslide Classification from Satellite Radar
## 4 Discussion
### SAR Frequency Band  
Sentinel-1a C-band SAR was used in this study because it had good temporal coverage in the months prior to the 2015 Gorkha earthquake, the short 12-day revisit time improves temporal coherence, the data are easily and openly available, and frequent coverage means that this satellite constellation may be used in future emergency response. Many SAR-based landslide studies also use either X-band or L-band SAR [21; 22; 23; 24; 26; 27; 28; 29]. X-band SAR has a shorter wavelength than C- and L-band, which makes it able to detect smaller ground movements at higher spatial resolution. However, due to increased penetration of SAR microwaves through the canopy at longer wavelengths, L-band SAR retains higher temporal coherence than C-band in vegetated regions [21; 24; 25], whilst coherence of X-band SAR imagery for the same regions is very low [21; 29]. Since all three methods discussed here rely on non-landslide pixels having a reasonable background coherence, we did not attempt to use X-band data for our case study, due to the heavy vegetation cover in our target region. Whilst the higher temporal coherence of L-band SAR may provide a significant advantage in vegetated regions, this may be negated if there is a long time between image acquisitions. When testing the ARIA method using L-band imagery collected by the ALOS-2 satellite system for the Gorkha earthquake, [PERSON] et al. [21] found L-band SAR to perform poorly in rural, vegetated areas. In order to obtain the two pre-event and one co-event images required for their method, [PERSON] et al. [21] were required to use imagery spanning 7 months. Over this time, vegetated regions underwent considerable change, resulting in changes in coherence unrelated to landsl or earthquake damage. Since the Gorkha earthquake occurred early in the lifespan of ALOS-2, the pre-event imagery acquired was not sufficient for the sibling identification required by the Bx-S method presented here, so it could not be tested with L-band SAR. However, for future events with more complete pre-event image acquisition, the increased coherence of L-band SAR in vegetated regions may increase the classification ability of SAR-coherence-based methods. Equally, these methods may perform even better with C-band SAR for regions that are less densely vegetated than our case-study area.
# Nicotinamide Adenine Dinucleotide (NAD) Metabolism as a Relevant Target in Cancer
## 8 Other NAD+-Dependent Enzymes in Cancer
### SIRTs in Cancer  
The sirtuin family (SIRT1-7) regulates the activity of histones, DNA repair and transcription and other signalling proteins by deacetylating several substrates. Therefore, its role as a tumour suppressor or oncogene has been described to be dependent on the type of cancer [22; 23; 79]. The best characterised member is SIRT1, which is associated with longevity. Resveratrol, a powerful antioxidant and antiaging compound, is capable of increasing NAD+ levels, inducing the activation of SIRT1 [22]. This enzyme is also activated by caloric restriction, which reduces the risk of cancer [2; 16]. SIRT1 activity is highly dependent on the NAD+/NADH ratio, rather than only the levels of NAD+, and can be inhibited by high levels of NAM [28; 80] or by DBC1, an endogenous SIRT1 inhibitor. Many studies suggest that SIRT1 also acts as a tumour suppressor. SIRT1 has been shown to inhibit the alpha subunit of hypoxia-inducible factor 1 (HIF-1\(\alpha\)), one of the factors that can activate glycolysis [28]. However, high levels of SIRT1 were found in some tumours, in which NAMPT activates SIRT1 expression by increasing NAD+ levels and decreasing NAM [69; 81]. SIRT1 regulates other transcription factors, such as p53, FOXOO3, E2F1, c-MYC and NF-\(\kappa\)B. P53 regulates cell cycle arrest and apoptosis and can also inhibit G6 PDH in the pentose phosphate pathway. SIRT1 can deacetylate and inhibit p53, thus allowing survival by ensuring the production of NADPH and ribose-5-P [17; 82]. The proto-oncogene C-MYC can induce NAMPT expression and inhibit DBC1, thereby activating SIRT1. In turn, SIRT1 activates c-MYC by deacetylation, generating a positive feedback loop [29; 81]. In addition, SIRT1 activates FOXOO3 by deacetylation, contributing to resistance to oxidative stress [64; 83]. Thus, the oncogenic role of SIRT1 depends on NAD+ levels. The other members of the sirtuin family, SIRT2-7, similar to SIRT1, can also act as oncogenes or tumour suppressors according to the type of tumour.
## 3 Proposed Beam Learning with MARL
### Proposed Algorithms
#### 3.4.1 Proposed User Clustering through Beam Sweeping  
A straightforward clustering approach based on a simple beam sweeping technique is proposed. Users with similar channel characteristics are grouped together and served by a single beam. This method allows the learning of interference-free arbitrary beams using multiple agents within the MARL framework. Additionally, clustering divides the complex task of finding beams across the entire azimuth into parallel sub-tasks, making it more manageable and efficient for developing a multi-user, multi-beam beamforming codebook. This simplification streamlines the acquisition process of a codebook comprising multiple beams. In this work, user clustering is proposed as part of the initial access procedure. The system begins with a traditional beam sweeping initial access procedure, as described in Section 3.1 of Section 3, and updates the codebook as learning progresses. A new UE requesting channel access is assumed to fall into a cluster corresponding to the initial access beam index aligned with that UE. Once sufficient learning is achieved through MARL, the codebook remains fixed in that learned state until further macro changes in the environment occur. The number of UE channel clusters and the number of beams are the same for both the untrained and trained codebooks because, in the proposed MARL algorithm, each beam in a learned codebook corresponds to one beam in the initial codebook. Hence, irrespective of the state of learning of the MARL agents, a new user is assigned to the cluster corresponding to the beam index it obtains through the initial access procedure.
# Real-Time AI-Based Informational Decision-Making Support System Utilizing Dynamic Text Sources
## 5 Text Classifier and Pre-Processor
### Multiclass Labeled Data  
Topic modeling efficiently analyzes large volumes of text by clustering documents into topics. With a large volume of unstructured data where the corpus has unlabeled meanings, we will not be able to apply our labeling approaches to create ML or DL models for these datasets. If we have unlabeled data, then we need to discover labels. In the case of text data, a cluster of documents is grouped by topic. LDA, an unsupervised generative probabilistic method for modeling a corpus, is the most commonly used topic modeling method [31]. It assumes that each document can be represented as a probabilistic distribution over latent topics, and assumes that topic distributions in all documents share a common Latent Dirichlet prior. Each latent topic in the LDA model is also represented as a probabilistic distribution of words, and the word distributions of the topics share this prior.  
Given corpus \(D\) consisting of \(L\) documents, with document d having \(N_{d}\) words (\(d\in~{}1,\ldots,L\)), LDA models D according to the following generative process [31]:  
* Multinomial distribution \(\varphi_{t}\) for topic \(t\)\((t\in\{1,\ldots,T\})\) from a Dirichlet distribution with parameter \(\beta\),
* Multinomial distribution \(\theta_{d}\) for document \(d\)\((d\in\{1,\ldots,L\})\) from a Dirichlet distribution with parameter \(\alpha\), and
* For a word, \(w_{n}(n\in\{1,\ldots,N_{d}\})\), in document \(d\).  
In the above generative process, words in documents are only observed variables, while others are latent variables (\(\varphi\) and \(\theta\)) and hyper-parameters (\(\alpha\) and \(\beta\)) [31]. To infer  
Figure 7: Sentiment analysis data in a scatter graph and a bar graph. (**a**) Polarity and subjectivity are shown in a scatter plot; (**b**) Sentiment analysis presents value counts in a bar graph.  
the latent variables and hyper-parameters, the probability of observed data \(D\) is computed and maximized as follows [31]:  
\[p(D|\alpha,\beta)=\prod_{d=1}^{L}\int p(\theta_{d}|\alpha)(\prod_{n=1}^{N_{d}} \sum_{Z_{dn}}p(Z_{dn}|\theta_{d})p(w_{dn}|Z_{dn},\beta))d\theta_{d} \tag{1}\]  
We divided the COVID-19 dataset into seven topic classes based on document similarity from the unstructured raw data. In Figure 8, Topic 5 has the most sentences from the whole corpus in the documents' sentence distribution graph. In contrast, Topic 6 has the fewest from among the classes.
## 2 Materials and Methods
### Considering the Effects of Temperature on SIF-Based GPP Models  
To avoid the impact of low solar illumination, measurements that were collected at a solar zenith angle (SZA) > 80\({}^{\circ}\) were excluded from the analysis. Raw SIF data that were outside the range \(\mu\pm 3\sigma\) (\(\mu\) and \(\sigma\) are the mean and standard deviation, respectively) were also excluded from the diurnal relationship analysis. Daytime measurements were averaged to daily resolution values.  
In order to better evaluate the potential impact of temperature on SIF-based GPP estimation, we further explored whether considering the temperature-corrected function could improve the performance of SIF-based GPP estimation. Low air temperature can decrease enzyme activity and slow the process of photosynthesis. Although temperature stress function may have different categories, the normalized temperature scalar (\(f(T_{a})\)) has been widely used to present temperature constraints to photosynthetic activity in LUE models (e.g., MOD17) [62; 63]. Various environmental factors limit photosynthetic activity. The \(f(T_{a})\) was a simple normalization of temperature, which was used as a temperature scalar to statistically evaluate the influences of temperature on the SIF-based GPP estimation. In order to better evaluate the potential impact of temperature on SIF-based GPP estimation, we explored whether considering \(f(T_{a})\) could improve the performance of SIF-based _GPP_ estimation [64]. To avoid zero values, we integrated two coefficients (0.95 and 0.05) into the temperature-corrected function and defined it as \(f(T_{a})\), showing the impacts of air temperature on the \(GPP-tSIF\) relationship.  
\[f(T_{a})=0.95*\frac{T_{a}-T_{a,min}}{T_{a,max}-T_{a,min}}+0.05 \tag{17}\]  
where \(T_{a,min}\) is the minimum temperature over the whole growing period and \(T_{a,max}\) is the maximum temperature over the whole growing period. Based on the Equations (1), (15) and (16), the estimation of \(GPP\) by \(tSIF\) can be expressed as:  
\[GPP_{F}=a\times tSIF \tag{18}\]  
Figure 3: Seasonal variations in daily mean air temperature (\(T_{a}\), **(a)**) The red dotted line in Panel (**a**) represents the biological zero. The seasonal changes of the photosynthetically active radiation (\(PAR\), **(b)**). The shadow represents the overwintering period. Local regression was used to smooth the data. The 95 percent confidence interval is indicated by the black shaded area.  
where the coefficient \(a\) represents the fitting parameters, \(GPP_{F}\) is the \(GPP\) estimation based on \(tSIF\). By considering the influence of the temperature-corrected function, the \(GPP\) estimation based on \(tSIF\) can be expressed as:  
\[GPP_{F-T_{a}}=a\times f(T_{a})\times tSIF \tag{19}\]  
where \(GPP_{F-T_{a}}\) is the \(GPP\) estimation based on \(tSIF\) with considering the effects of temperature. Therefore, we can compare the performance of the \(GPP\) estimation based on SIF with or without considering the effects of temperature.
## 4 Types of FL
### Differential Privacy and Homomorphic Encryption in FL  
Differential privacy and homomorphic encryption are closely related to all forms of federated learning because they solve two of the most important issues in FL: data privacy and security.  
In horizontal and vertical FL (described in the previous subsections), numerous entities or devices collaborate to train models while protecting the local data's privacy. To prevent data leaks during this procedure, differential privacy assures that individual data points are not rebuilt from shared model updates. Furthermore, homomorphic encryption enables calculations on encrypted data, which is critical when the data between different institutions must remain secure (for example, between hospitals or banks collaborating in a vertical FL environment). These techniques safeguard each participant's privacy.  
Federated transfer learning (FTL) tries to adapt models across disparate datasets, such as healthcare and finance, when the data are sparse or from distinct domains. Differential privacy and homomorphic encryption are critical in allowing enterprises to exchange insights and models without disclosing the underlying private data. The safe transmission of model updates enabled by these techniques assures compliance with privacy rules such as GDPR, while also protecting the data's proprietary nature.  
Some practical examples for each type of FL, demonstrating how differential privacy and homomorphic encryption are used in real-world scenarios, are as follows. For instance, in vertical FL (where two organizations may share different aspects of the same users), homomorphic encryption can provide secure computation throughout encrypted data without the need for the data to be decrypted. In horizontal FL, differential privacy ensures that user-specific data on mobile devices cannot be reconstructed from the global model.