esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 The Design and Application of MESs
### MECs for Biofuel Production and Chemical Synthesis  
Microbial electrolysis cells (MECs) incorporate electrotrophic microorganisms at the cathode, which utilise it as an electron donor. These microbes catalyse thermodynamically expensive electrolysis reactions so that the MEC can synthesize biofuels or commodity chemicals in an energetically favourable manner [5]. Some of the biofuels produced include hydrogen, methane, butanol and ethanol, and the commodity chemicals generated include glycerol, acetate and 2-oxobutyrate [4; 5; 20; 122; 123; 124]. Currently known electrotrophs are _Sporomusa ovata, Geobacter sulfurreducens, Clostridium ljungdahlii_ and _Morella thermoacetica_[4; 122]. Microbial electrosynthesis (ME) technology utilised in MECs relies on these microbes to receive electrons from the cathode, which they subsequently use to reduce carbon dioxide and form useful chemical by-products such as biofuels and can also fix carbon, which is beneficial to reducing greenhouse gases [4; 122].  
As with MFCs, MECs have a variety of architectures, with the most common being two-chamber (2 CMEC) and single-chamber (SCMEC) MECs. The main advantage of the 2 CMEC system is its higher hydrogen production ability [20]. Methane yields of 80.9 mL/L from 2 CMEC systems have been reached in concert with the modification of the cathode with the addition of graphite felt to carbon rods to form a hybridised biocathode [125].  
Although lower in hydrogen production, the membrane-free SCMECs are less costly than 2 CMECs, particularly if hydrogen production can be improved by using continuous rather than batch mode operation. The application of membrane-free SCMECs in the fermentation of organic matter for energy recovery has resulted in improvements to the overall stability of anaerobic digestion processes while also improving the performance of energy recovery [126].
## 4 The Proposed Key Distribution Framework
### A Multipath-Multi-Key Distribution Algorithm  
#### 4.2.1 Path Selection Criteria  
The algorithm for selecting trusted paths addresses the K max-min problem through a two-phase approach, employing the KSP algorithm, specifically [PERSON]'s algorithm, to initially identify a diverse set of candidate paths that minimize common security threats. The second phase uses a modified greedy algorithm to finalize the selection, prioritizing path isolation to reduce simultaneous compromises. This method leverages dynamic link weighting based on real-time network data and historical security incidents to adjust path classifications [23; 38; 39].  
Assuming that the source node is \(S\), the destination node is \(D\), and there are \(k\) paths that need to be selected, the specific steps are outlined in Algorithm 1.  
In particular, correlation (Cor) measures the overlap of risk factors between paths, such as shared physical infrastructure or geographical proximity, which could expose paths to common vulnerabilities. In our model, we consider the correlation among all involved paths to ensure a comprehensive evaluation of path suitability. This consideration helps maintain a balance between path diversity and network resource utilization. Balancing these factors is essential for efficient and secure key distribution [29]. The correlation of a set of paths is defined as follows:\[\text{Cor}_{S(P)}=\frac{\sum_{L\in S(L_{\text{comm}})}(\text{freq}_{L}-1)}{\sum_{ P_{i}\in S(P)}\text{card}(S(L)_{P_{i}})} \tag{1}\]  
where \(S(P)\) represents the set of paths, \(\text{freq}_{L}\) represents the number of paths containing the link \(L\), and \(\text{card}(S(L)_{P_{i}})\) represents the number of links on the path \(i\). In addition, the correlation threshold (\(TH_{\text{Corr}}\)) is a dynamically adjusted threshold that helps to determine acceptable risk levels for path correlations, allowing the system to adapt to varying security needs and external conditions [40]. This threshold is determined using a scaling factor (\(\lambda_{\text{Cor}}\)), with a value range between \((0,1)\), applied to the average correlation, \(\overline{\text{Cor}}\), and can be formulated as follows:  
\[TH_{\text{Corr}}=\lambda_{\text{Cor}}\cdot\overline{\text{Cor}} \tag{2}\]
## 5 Evaluation
### Datasets
#### 5.1.1 Random Selection Gold Standard Dataset  
The data were collected by jobchannel AG (Thalwil, Switzerland) ([[https://www.jobchannel.ch](https://www.jobchannel.ch)]([https://www.jobchannel.ch](https://www.jobchannel.ch)), accessed on 1 March 2024), a company focused on aggregating job data from online sources (e.g., boards, websites, etc.). The jobchannel ontology describes the various skills necessary for the Swiss job market. Each skill is split into a (predicate, topic) pair representing the action and the context in which the skill is performed.  
The skill \"Marketingkonzept planen\" (plan marketing concepts), for example, comprises the topic \"marketing concepts\" and the predicate \"plan\".  
Human annotators used the following guidelines based on Swiss practices for manually assessing skills with a binary value for offshorability and automatability:  
1. Offshorability determines if a task can be performed regardless of the physical presence of the person performing it. Outsourcing issues may arise due to location dependencies, human interaction (e.g., especially if cultural preferences are considered), or even the need to move around large objects.
2. Automatability assesses whether technology (e.g., robots, drones, software, etc.) can currently fully perform the task. Activities are non-automatable if they are not clearly specified or need too much human interaction.  
Table 8 summarizes the dataset statistics. The experts labeled 67.7% of the examined skills as offshorable and 58.5% as automatable. Some examples of expert assessments can be found in Table 9.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Architecture** & **Opt. Threshold (Offshorability)** & **Opt. Threshold (Automatability)** \\ \hline FC Layers \#1 & 0.58 & 0.56 \\ FC Layers \#2 & 0.60 & 0.56 \\ FC Layers \#3 & 0.58 & 0.55 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Assessment of the optimal threshold for the ROC Curves of the networks  
\begin{table}
\begin{tabular}{l l c c c} \hline \hline
**Corpus** & **Offshorable** & & **Automatable** & **Total** \\  & **Yes** & **No** & **Yes** & **No** & **Annotations** \\ \hline Random selection & 1526 & 728 & 1319 & 935 & 4508 \\ Rule-based & 5280 & 602 & 2577 & 5478 & 13,937 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Class distribution within the gold standard datasets. Please note that the rule-based dataset frequently only provides labels for only one of the classes (i.e., only offshorable or automatable).  
\begin{table}
\begin{tabular}{l l c c c} \hline \hline
**Predicate** & **Topic** & **Offshorability** & **Automatability** \\ \hline create & dossier & 1 & 1 \\ correct & jaw malposition & 0 & 0 \\ program & user interface & 1 & 0 \\ clean & object & 0 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Example expert annotations for both offshorability and automatability.
## 3 Simulation Setup and Results under Ideal Assumptions
### Performance with Ideal Assumptions
Concerning the two considered DP variants, both schemes perform very similarly in the three cases (all the UEs, only UEs in LoS and only UEs in NLoS), but the DP FS attains slightly superior performance when compared to DP NFS, due to its higher flexibility in scheduling different sets of UEs in different RBs. Finally, average user throughput, 5 th percentile of the user throughput and average cell throughput values are compared in Table 3 for all the simulated algorithms. Table 3 also shows the gains with respect to a scheme without spatial multiplexing (i.e., ABF with \(P=1\)), considered here as the baseline. This comparison shows the significant benefit of spatially multiplexing multiple users in each subframe. Focusing, for example, on the case of the ABF with four RF chains, we observe that the gain in the average user throughput is about 150% when compared to the baseline scheme with only one RF chain. Results also highlight the main limitation of the ABF schemes, which, while able to approach the performance of the DP in terms of average user and cell throughput, cannot provide large gains for the 5 th percentile user throughput, even when the number of RF chains \(P\) increases. Figure 4: CDF of user throughput for the ABF and DP evaluated schemes considering only line of sight (LoS) users. Figure 5: CDF of user throughput for the ABF and DP evaluated schemes considering only non line of sight (NLoS) users.
# Improving Passband Characteristics in Chebyshev Sharpened Comb Decimation Filters
## 2 Compensators: Applications and Design
### Design
#### 2.2.2 Wideband Design  
In the wideband design the passband frequency \(\omega_{p}\) equals \(\pi/4M\leq\omega_{p}\leq\pi/2M\). We consider two wideband sinusoidal compensators.  
Extension of Narrowband Design  
The magnitude response is given as [14]  
\[C_{2}\Big{(}e^{jM\omega}\Big{)}=\bigg{[}1+C_{21}sin^{2}\bigg{(}\frac{\omega M}{ 2}\bigg{)}\bigg{]}\times\bigg{[}1+C_{22}sin^{4}\bigg{(}\frac{\omega M}{2} \bigg{)}\bigg{]}, \tag{6}\]  
where \(C_{21}\) and \(C_{22}\) are the parameters of the design.  
The system function of this compensator is given as follows [14]:  
\[\begin{array}{c}C_{2}(z)=2^{-2}\big{[}\big{(}-1+2z^{-1}-z^{-2}\big{)}C_{21}+ 2^{2}z^{-1}\big{]}\times\\ \Big{[}2^{-4}C_{22}\Big{[}\big{(}1+z^{-4}-4\big{(}z^{-1}+z^{-3}\big{)}+\big{(}2 ^{2}+2\big{)}z^{-2}\Big{]}+z^{-2}\Big{]}.\end{array} \tag{7}\]  
The compensator requires nine adders and two multipliers \(C_{21}\) and \(C_{22}\). The number of the required adders in a multiplierless design is given as  
\[N_{2}=9+N_{21}+N_{22}, \tag{8}\]  
where \(N_{21}\) and \(N_{22}\) are the number of adders in SPT forms of \(C_{21}\) and \(C_{22}\), respectively.  
To increase the flexibility of the design, we also consider the less complex compensator from [15] introduced in the next subsection.  
Simplified Version of Compensator in [14]  
The magnitude response is given as follows [15]:  
\[C_{3}\Big{(}e^{jM\omega}\Big{)}=1+C_{31}sin^{2}\bigg{(}\frac{\omega M}{2} \bigg{)}+C_{32}sin^{4}\bigg{(}\frac{\omega M}{2}\bigg{)}, \tag{9}\]  
where \(C_{31}\) and \(C_{32}\) are parameters of the design.  
The system function is given as [15],  
\[C_{2}(z)=2^{-4}C_{32}\Big{(}1-2z^{-2}\Big{)}-(C_{31}+C_{32})2^{-2}\Big{(}z^{- 1}-2z^{-2}+z^{-3}\Big{)}+z^{-2}. \tag{10}\]  
The compensator requires six adders, two multipliers, \(C_{32}\), and the sum of \(C_{32}\) and \(C_{31}\). The number of the needed adders in a multiplierless design is  
\[\text{N}_{3}=6+N_{32}+N_{312}, \tag{11}\]  
where \(N_{32}\) and \(N_{312}\) are the number of adders in SPT forms of \(C_{32}\) and the sum of \(C_{31}\) and \(C_{32}\), respectively.
## 3 Link Level Parameters and Channel Coefficient Generation
### Coefficient Generation
Therefore, the total time-invariant channel matrix-valued function for the \(s\)-th sector \(\mathbf{H}_{s}^{NLOS}(\tau)\) can be obtained by summing all the matrices from each ray of each cluster in the following way:  
\[\mathbf{H}_{s}^{NLOS}(\tau)=\sum_{n=1}^{2}\sum_{i=1}^{3}\sum_{m\in\mathbb{R}_ {i}}\mathbf{H}_{n,m,s}^{NLOS}\delta(\tau-\tau_{n,i})+\sum_{n=3}^{N_{cl}}\sum_ {m=1}^{M_{ray}}\mathbf{H}_{n,m,s}^{NLOS}\delta(\tau-\tau_{n}) \tag{59}\]  
In the case of _LOS_ presence, we define the _LOS_ channel matrix as:  
\[\mathbf{H}_{s}^{LOS}= \begin{cases}g_{LOS}\ \mathbf{a}_{rx}(\theta_{LOS,ZOA}^{ \prime},\phi_{LOS,AOA}^{\prime})\mathbf{a}_{tx,s}^{H}(\theta_{LOS,ZOD}^{ \prime},\phi_{LOS,AOD}^{\prime})&\quad\text{for single pol.}\\ \Big{[}\mathbf{a}_{rx}(\theta_{LOS,ZOA}^{\prime},\phi_{LOS,AOA}^{\prime}) \mathbf{a}_{tx,s}^{H}(\theta_{LOS,ZOD}^{\prime},\phi_{LOS,AO}^{\prime})\Big{]} \otimes\mathbf{G}_{LOS}&\quad\text{for dual pol.}\end{cases}  \tag{60}\]  
with  
\[g_{n,m}=F_{rx,\theta}(\theta_{LOS,ZOA},\phi_{LOS,AOA})\ F_{tx,\theta}(\theta_{ LOS,ZOD},\phi_{LOS,AOD}) \tag{61}\]  
and  
\[\mathbf{G}_{n,m}= \begin{bmatrix}F_{rx,\theta}^{+\xi}(\theta_{LOS,ZOA},\phi_{LOS,AO})& F_{rx,\phi}^{+\xi}(\theta_{LOS,ZOA},\phi_{LOS,AOA})\\ F_{rx,\theta}^{-\xi}(\theta_{LOS,ZOA},\phi_{LOS,AOA})&F_{rx,\theta}^{-\xi}( \theta_{LOS,ZOA},\phi_{LOS,AOA})\end{bmatrix}\begin{bmatrix}1&0\\ 0&-1\end{bmatrix}  \tag{62}\] \[ \begin{bmatrix}F_{tx,\theta}^{+\xi}(\theta_{LOS,ZOD},\phi_{LOS, AOD})&F_{tx,\theta}^{-\xi}(\theta_{LOS,ZOD},\phi_{LOS,AOD})\\ F_{tx,\theta}^{+\xi}(\theta_{LOS,ZOD},\phi_{LOS,AOD})&F_{tx,\theta}^{-\xi}( \theta_{LOS,ZOD},\phi_{LOS,AOD})\end{bmatrix} \]In this case, the final matrix-valued channel impulse response is given by adding the the _NLOS_ and _LOS_ component and scaling both terms by the Ricean factor \(K_{R}\). The _LOS_ component is associated with the shortest possible delay of all \(\tau_{n}\), i.e., \(\tau_{1}\):  
\[\mathbf{H}_{\mathrm{s}}^{\mathrm{LOS}}(\tau)=\sqrt{\frac{1}{K_{R}+1}}\mathbf{H }_{\mathrm{s}}^{NLOS}(\tau)+\sqrt{\frac{K_{R}}{K_{R}+1}}\mathbf{H}_{\mathrm{s} }^{LOS}(\tau-\tau_{1}). \tag{63}\]  
As final step, we apply path loss and shadowing for all the channel coefficients. After selecting the sampling frequency \(f_{s}\), the channel impulse response can be sampled and the total number of necessary taps \(N_{taps}\) to represent the channel can be computed as:  
\[N_{taps}=1+\max_{n}\left\lfloor\tau_{n}\ f_{s}+\frac{1}{2}\right\rfloor \tag{64}\]  
the total sampled channel \(\mathbf{\mathcal{H}}_{s}\) can be represented as a \(N_{r}\times N_{t}\times T\) tensor of order 3 per each sector.
## 3 System Design and Implementation
### Implementation
#### 3.3.2 Raspberry Pi 2  
The choice of hardware technology plays a significant role because of its usefulness and commercial availability; however, it is also constrained by the cost. For the specified reasons, we chose Raspberry Pi 2 model B, a credit card-sized microcomputer with basic hardware processing, as shown in Figure 3B. It is relatively cheap, consumes less power compared to other microcomputers, and is widely available [22; 23; 24]. Raspberry Pi acts as a processing unit that acquires user data through a keypad and the data about the location from a GPS sensor. It then passes the acquired data to the client application, which is running on a Linux Debian operating system. It is equipped with  
Figure 3: Hardware used in designing an emergency response system.  
a powerful application processor [25; 26; 27]. The client-end sends 4 \(\times\) 4 keypad's user-data and the GPS sensor's location data serially to the General Purpose Input/Output (GPIO) pins of the Raspberry Pi. This data then gets manipulated through a client application, whereas the server receives this data using web sockets.
## 2 Materials and Methods
### NUCAPS
For instance, both the IR + MW and MW-only retrievals were successfully used to examine the diurnal cycle of tropical storms [17]. Aside from the cloud products, both the IR + MW and MW-only NUCAPS products are reported on a fixed vertical grid. The fixed vertical grid consists of 100 pressure levels for temperature retrievals and 100 layers defined by the level boundaries for water vapor and other trace gases, which are retrieved as a column density. The NUCAPS layer pressure (\(P_{L}\)) is defined to be the midpoint of the pressure levels (\(P_{L}\)) above and below, which varies logarithmically with height:  
\[P_{L^{\prime}}=\overline{P_{L}}=\frac{P_{L}-P_{L-1}}{\log\Bigl{(}\frac{P_{L}} {P_{L-1}}\Bigr{)}} \tag{1}\]  
The fixed vertical grid was chosen to ensure the data are stored consistently in files for all scenes without introducing gridding errors. These conditions are satisfied by reporting NUCAPS on the atmospheric transmission parameterization levels and layers in Standalone AIRS Radiative Transfer Algorithm (SARTA) [18; 19], which is the radiative transfer algorithm employed by the NUCAPS IR + MW retrievals. The MW-only radiative transfer algorithm uses a method derived and updated by [20; 21; 22], which is then gridded to the 100 levels for comparability. Even for successful retrievals, IR and MW measurements have varying signal-to-noise as a function of pressure and by scene. Thus, the IR + MW contribution to the final retrieval is determined by their information content [14]. The information content is scene-dependent and is highest where cloud clearing errors are small, background thermal vertical gradients are large, and over the ocean [23]. Diagnostic parameters within NUCAPS can determine the vertical resolution of the final retrieval. NUCAPS was designed to be satellite agnostic and capable of processing sounder data from a variety of satellites, including the CrIS/ATMS instruments on the Suomi NPP and NOAA-20 satellites, IASI/AMSU on MetOp-A/-B/-C, and AIRS/AMSU on Aqua. Of these, NOAA operationally supports NOAA-20 but also archives NUCAPS retrievals produced from the MetOp series satellites for research purposes. NOAA previously processed NUCAPS from Suomi NPP operationally but since 2021 has discontinued support due to IR band failures. In addition to delivery via SBN to NWS weather forecast offices (WFOs), NUCAPS is publicly distributed via the NOAA Comprehensive Large Array-data Stewardship System (CLASS), which is a database of environmental data. In this study, we ran an offline version of NUCAPS to perform an information content analysis, because while these fields are produced within NUCAPS, they are not written to the operational EDR file. At present, the NOAA Center for Satellite Applications and Research (STAR) is adding the necessary data to the EDR file, which will become publicly available for the full record in 2023.
## 2 Materials and Methods
### Statistical Analysis
A false negative was defined as no birth alert received but a newborn calf being sighted in the paddock. The accuracy, sensitivity, specificity and precision of the birth alert systems was assessed using a binary classification with the following equations [23]. \[\text{Accuracy}=(\text{TP+TN})/(\text{TP+TN+FP+FN})\]  
\[\text{Sensitivity}=\text{TP}/(\text{TP+FN})\]  
\[\text{Specificity}=\text{TN}/(\text{TN+FP})\]  
\[\text{Precision}=\text{TP}/(\text{TP+FP})\]  
All data from the GNSS collars and VIT were trimmed for the analysis 30 days either side of calving. Linear mixed-effects regression models were used to analyze the GNSS and activity data in relation to the day of calving using the '1 me4' package in R, version 3.6.3 [24], with the day from calving as the dependent variable and the cow ID and date included as random effects. Differences between means were calculated for the day of calving and day after calving against all other 59 days around calving using the Bonferroni adjustment. Temperatures of the VIT were removed from the analysis below 37.13 \({}^{\circ}\)C and above 40.76 \({}^{\circ}\)C in Trial 1, and 37.60 \({}^{\circ}\)C and 40.91 \({}^{\circ}\)C in Trial 2 in agreement with previous studies [25, 26, 27, 28]. Temperature outside these ranges were confirmed to be during periods when the VIT was outside the cow through visual checking of VIT in the paddock and VHF signal of VIT inside each cow. The 'Move' package [29] was used to calculate the distance travelled between each 30 min fix of the GNSS data. Daily distance travelled was calculated as the sum of each 30 min distance per day, daily average activity of the X- and Y-axis counts measured by the accelerometer, and the daily average VIT temperature for each cow was calculated. Only data from cows with confirmed date of births, as described above, were used for the analysis of distance travelled and activity relative to day of calving. Distance travelled was log transformed in both trials before analysis to normalize the data and to obtain P values; however, the mean values were presented in their original units. Only days significantly different from the day of calving and day 1 after calving were presented for distance and activity graphs, to account for animals calving at different times on the day of calving. The two birth alert systems were also compared using logistic regression with the brand as the only fixed effect and alert received or not as the dependent variable using the 'glm' stats package (R version 3.6.3) [24].
# A Novel Routing Scheme for Creating Opportunistic Context-Virtual Networks in IoT Scenarios +
## 5 SACAR Algorithm Description
### SACAR Base Algorithm
```
1:An IoT scenario: \(\mathcal{D}\), simulation time: \(T\), frequency \(\omega_{t}\), update time interval: \(\delta_{t}\)
2:\(t=0\)\(\triangleright\) Current time instant
3:do
4:for all node \(d_{i}^{x_{i}y_{i}}\in\mathcal{D}\)do
5:\(d_{i}^{x_{i}y_{i}}\mathit{listen}()\)\(\triangleright\) Listen if incoming messages arrive
6:if \(\omega_{t}\) is triggered then
7:\(\mathcal{N}_{i}\gets d_{i}^{x_{i}y_{i}}\mathit{getNeighbors}(\mathcal{G})\)
8:for all neighbor node \(d_{j}^{x_{i}y_{j}}\in\mathcal{N}_{i}\)do
9:\(sendContextId(G_{i},S_{i},x_{j}^{x_{i}y_{j}})\)
10:endfor
11:endif
12:if\(\delta_{t}\) is triggered then
13:if\(S_{i}\
eq\emptyset\)then
14:for all skill \(s_{p}^{i}\in S_{i}\)do
15: execute \(f(s_{p}^{i})\)
16:endfor
17:endif
18:endfor
19:while\(t<T\)
```  
**Algorithm 1** SACAR pseudocode description (i): share context IDs and skills' execution.
# Wireless Accelerometer Architecture for Bridge SHM: From Sensor Design to System Deployment
## 2 Wireless Sensor Network Architecture
### Repeater  
The repeater shares the same structural framework as the sensor node, consisting primarily of an ESP32 microcontroller (equipped with an external Wi-Fi antenna) and a power management unit. However, unlike the sensor node, the repeater does not include additional sensors for data collection and the GNSS module. Its primary function is to extend the network coverage by relaying data between distant sensor nodes and the gateway; to do this, the repeater node acts as a hotspot generator.  
The shared structure between the sensor nodes and the repeater facilitates scalability within the network architecture. By adopting a common design framework, both devices can be easily replicated and deployed across the network as needed, while keeping design and production costs low.  
Figure 1: Three-dimensional model of the described printed circuit board (PCB).