esa-sceva/satcom-chunk-collection huggingface dataset:
# Insider Threat Detection Based on Deep Clustering of Multi-Source Behavioral Events
## 4 Experiment
### Evaluation Metrics  
When evaluating a detection system, the results can be categorized as either correct or incorrect, with all possible outcomes falling into the following four scenarios:  
1. True positive (_TP_): the sub-activities of abnormal behavior are detected as abnormal;
2. True negative (_TN_): the sub-activities of normal behavior are detected as normal;
3. False positive (_FP_): the sub-activities of normal behavior are detected as abnormal, resulting in a false alarm;
4. False negative (_FN_): the sub-activities of abnormal behavior are detected as normal, resulting in a missed detection.  
If \(TP\), \(TN\), \(FP\), and \(FN\) represent the number of occurrences of the aforementioned scenarios, the following metrics can be defined:  
\[\textit{False Positive Rate }(FPR)=\frac{FP}{TN+FP}\]  
\[\textit{True Positive Rate }(TPR)=\frac{TP}{TP+FN}\]  
For each threshold set, the corresponding values of \(FPR\) and \(TPR\) are obtained. By varying the threshold from 0 to the maximum, initially every behavior sub-activity is predicted as an anomaly. As the threshold increases, the number of predicted anomalous sub-activities decreases until none are left. During this process, \(FPR\) and \(TPR\) values are calculated at each step, and a graph is plotted with \(FPR\) as the \(x\)-axis and \(TPR\) as the \(y\)-axis, known as the \"ROC curve\". By plotting the ROC curves of different detectors on the same coordinate system, it becomes possible to visually evaluate their performance. Detectors with ROC curves closer to the top-left corner represent higher accuracy.  
If two ROC curves do not intersect, the curve closer to the top-left corner represents a better-performing detector. However, in practical tasks, the situation is often more complex, and if the ROC curves intersect, it is challenging to make a general assertion about superiority. In such cases, if a comparison must be made, the Area Under the ROC Curve (AUC) is a reasonable criterion for comparison. AUC represents the overall performance of the detection model, with values closer to 1 indicating better performance and values closer to 0 indicating poorer performance. The calculation of AUC accounts for the detector's classification ability for both anomalous and normal behaviors, even in situations with imbalanced normal and anomalous samples. AUC is not sensitive to the balance of sample categories, which is why it is commonly used to evaluate the performance of anomaly detection models.
# Trepenoid Metabolic Engineering in Photosynthetic Microorganisms
## 2 Pathway Modules in Terpenoid Biosynthesis
### Modules III and IV: Terpene Syntheses, Skeleton Decorations, and Further Modifications  
The next two modules comprise the specific pathway for each target compound and are where the true terpenoid diversity is unlocked. Terpene synthases (Module III) form a wide variety of carbon skeletons. These skeletons are the substrates of the decorating enzymes of Module IV. This chemistry is highly complex and results in massive diversity. In this case, we outline generic information to provide (a) a more complete overview of terpenoid biosynthesis and (b) context for the following section on terpenoid production by photosynthetic organisms.  
Terpene synthases are often multimeric and slow enzymes [45]. These features make them challenging to use in heterologous production. The solution commonly employed is massive overexpression, which, while it can increase catalytic activity, also creates problems such as inclusion of body formation and loss of activity [46; 47; 48]. In plants, terpene synthases range in number from one (in the case of the moss _Physcomitella patens_) to over a hundred per genome [49]. They are thought to have evolved by descent from a single gene resembling the kaurene synthase found in moss and they are notoriously promiscuous enzymes with a high level of substrates and product flexibility [49; 50]. While undesirable in many cases (where a specific product is sought), these properties offer opportunities. Terpene synthases can be combined in heterologous systems to produce many different compounds including compounds which are new to nature and have desirable industrial properties [11; 12; 22; 51].  
Decorating enzymes--that alter the basic carbon skeleton--are usually the final step of terpenoid biosynthesis. Various enzymes such as oxidative enzymes (especially cytochromes P450, monooxygenases that add hydroxyl groups with a high degree of stereospecificity), methyltransferases, acyltransferases, and prenyltransferases diversify the already numerous terpene skeletons [1]. The cytochromes P450 are one of the keys to unlocking the large terpenoid diversity [5; 52]. CytochromeP450s due to their unique catalytic mechanism are able to introduce hydroxyl groups in a very precise manner even in substrates with a large amount of chiral carbons [53]. They require two electrons per reaction, which is usually provided by NADPH and via a specific cytochrome P450 redox partner even though several other electron carriers can drive the reactions forward [54; 55]. Cytochrome P450s are arguably challenging enzymes to engineer in heterologous hosts due to the need for redox power in the form of NADPH, cofactor availability--P450s contain heme molecules--and poor expression of eukaryotic P450s in bacteria [56; 57].
# Heterogeneous Wireless Networks for Smart Grid Distribution Systems: Advantages and Limitations
## 5 Performance Study
### Performance Gain of the Proposed Architecture  
As mentioned earlier, transferring data over two separate communication networks strengthens the robustness of the system. By using MPTCP, if a link is down or congested, data is pushed on the link of the best condition. In the following experiment, seven substations transmit data continuously to the primary substation. The experiment addresses a scenario where the secondary substations are covered by more than one 3G/4G cell but sharing the same WiFi network. Thus, the MPTCP protocol is able to consume more capacity from 3G/4G cells as the ACK packets can be sent over both links in contrary to the case where only a WiFi or a 3G/4G link is available. The maximum congestion window \(W_{M}\) of both WiFi and 3G/4G TCP subflows is 55 segments.  
Figure 2 compares the data transfer throughput performance of the single-path WiFi and 3G/4G link cases versus our proposed HWN-based architecture using the MPTCP protocol. The figure clearly shows that the HWN configuration improves the throughput to a point better than any of the underlying technologies can achieve on its own separately. As Figure 2 reveals, relying only on the WiFi network leads to a significantly low throughput because all the eight substations, including the primary substation, share the wireless medium. As a matter of fact, the primary substation communicates back an ACK message for each segment submitted by the other substations. This greatly reduces the available bandwidth and increases the chance for collisions. In case of using a 3G/4G link alone, too many ACK packets accumulate at the transmission buffer of the primary substation's transceiver (especially with high speeds). This leads to increased packet drops, although one ACK packet may acknowledge several received segments. On the contrary, a substantial improvement is noticed in Figure 2 with the HWN combining both networks using MPTCP. This is due to distributing the traffic between the interfaces according to the network congestion condition and link quality.  
\begin{table}
\begin{tabular}{c c} \hline \hline
3G/4G speeds & 384 kbps, 1 Mbps, 5.7 Mbps, 23 Mbps, \& 70 Mbps \\ Max congestion window size & 13, 31, 55, \& 65 \\ Data burst size & 5 KB, 500 KB, 1 MB, \& 2 MB \\ Number of substation & 3 \& 7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Configuration parameters.  
Figure 2: Throughput performance of single-path WiFi and 3G/4G versus MPTCP.
## 2 DC Fault Current Analysis
### DC Fault Analysis of the Test System
#### 2.2.2. Freewheeling Current and AC Feeding Current Period  
The fault current circuit after completely discharging IGBT blocks or capacitor to \(V_{conv}=0\) is depicted in Figure 6, in which the red dotted line indicates the AC feeding current, and the blue dash-dotted line represents the freewheeling current given the stored energy in the arm reactors. The equivalent circuits of the AC feeding and freewheeling DC currents are illustrated in Figure 7a,b, respectively. The fault current can be considered a superposition of the three-phase short-circuit fault and decayed freewheeling currents. Thus, the fault currents in the upper and lower arms can be defined as  
\[i_{pj}=i_{fwj}+i_{pj,ac} \tag{26}\]  
\[i_{nj}=i_{fwj}-i_{nj,ac} \tag{27}\]  
where \(i_{fwj}\) denotes the freewheeling current in each converter phase. \(i_{pj,ac}\) and \(i_{nj,ac}\) indicate the AC feeding current via the upper and lower arms of each phase, respectively. The analysis during the SM capacitor discharging period shows that the initial freewheeling current is equal to the peak value of the discharging current in each converter phase.  
\[I_{fw0}=\frac{1}{3}I_{dis\_peak} \tag{28}\]  
Figure 6: Diagram of the DC pole-to-pole fault circuit after blocking SMs.  
Figure 7: Equivalent circuit of the (**a**) AC feeding current and (**b**) freewheeling DC current.  
Thus, the freewheeling current of each arm and the DC line current can be obtained using Equations (29) and (30).  
\[i_{fwj}(t)=I_{fw0}e^{-\frac{t}{\tau_{1}}} \tag{29}\]  
\[i_{dc}(t)=3I_{fw0}e^{-\frac{t}{\tau_{1}}} \tag{30}\]  
For example, the initial arm current of the freewheeling period of Phase A is expressed as follows:  
\[I_{pa\_bk}=I_{fw0}+\frac{1}{2}i_{sa}(t_{dis\_peak}) \tag{31}\]  
\[I_{na\_bk}=I_{fw0}-\frac{1}{2}i_{sa}(t_{dis\_peak}) \tag{32}\]  
Similarly, the arm currents can be decomposed as Equations (33) and (34).  
\[i_{pa}(t)=i_{fau}(t)+i_{pa\_ac}(t) \tag{33}\]  
\[i_{na}(t)=i_{fau}(t)-i_{na\_ac}(t) \tag{34}\]  
In reference to Equations (21) and (28), the arm currents can be defined as  
\[i_{pa}(t)=\frac{1}{3}I_{dis\_peak}e^{-\frac{t}{\tau_{1}}}+\frac{1}{2}\frac{ \sqrt{2}U_{s}}{Z_{eq}}\sin(\omega_{s}t+\theta_{a}-\varphi) \tag{35}\]  
\[i_{na}(t)=\frac{1}{3}I_{dis\_peak}e^{-\frac{t}{\tau_{1}}}-\frac{1}{2}\frac{ \sqrt{2}U_{s}}{Z_{eq}}\sin(\omega_{s}t+\theta_{a}-\varphi) \tag{36}\]  
\[\tau_{1}=\frac{2(2L_{0}+2L_{dc})}{2R_{dc}+R_{fault}} \tag{37}\]  
where \(\tau_{1}\) is the attenuation coefficient of the freewheeling current. Equivalent impedance is expressed in Equation (22), and the peak value of the discharging current can be obtained via Equation (15).
## 6 Propagation Time for a Signal, Emitted and Intercepted by Satellites on a Space-Oriented Orbit
### Second Formulation of the Theorem for Four-Dimensional Uniformization
#### 12.6.1 The First Equation  
The first equation in (275) can be written as  
\[\frac{\rho(z_{1})}{\rho_{1}(z_{2})+i\rho_{2}(z_{2})}=\frac{A_{1}+B_{1}}{\frac {-(A_{1}+B_{1})}{2}+i\frac{\sqrt{3}}{2}(A_{1}-B_{1})}. \tag{276}\]  
Denoting  
\[m:=\frac{\widetilde{A}_{1}-\bar{B}_{1}}{\widetilde{A}_{1}+\bar{B}_{1}}, \tag{277}\]  
the above equation can be represented as  
\[-\frac{\rho_{.}(z_{1})}{2}-\rho_{1}(z_{2})+i\frac{\sqrt{3}}{2}\rho_{.}(z_{1})m -i\rho_{2}(z_{2})=0. \tag{278}\]  
Setting up equal to zero the real and imaginary part, one can obtain  
\[\rho_{1.}(z_{2})=-\frac{\rho_{.}(z  
Using the group-theoretical law for summing up of points, represented by formulae (199) for expressing \(\rho_{\cdot}(z_{3})=\rho_{\cdot}(z_{1}+z_{2})\), the above equation can be written as  
\[\frac{\rho(z_{1})}{\rho(z_{1})+\rho(z_{2})+\frac{1}{4}\Big{(}\frac{\rho^{\prime} (z_{1})-\rho^{\prime}(z_{2})}{\rho(z_{1})-\rho(z_{2})}\Big{)}^{2}}\]  
\[=\frac{1}{-\frac{1}{2}-i\frac{\sqrt{3}}{2}m}. \tag{281}\]  
Using the two equalities in (279), Equation (281) can be represented as \(Q_{1}+iQ_{2}=0\), where the real part \(Q_{1}\) and the complex part \(Q_{2}\) are  
\[Q_{1}:=-3\rho^{3}(z_{1})(5m^{2}+3)-\frac{9}{4}(\rho^{\prime}(z_{1}))^{2}(1- \frac{1}{3}m^{2})\]  
\[+\frac{3}{4}\rho(z_{1})\frac{\partial m}{\partial q}.\frac{\partial q}{ \partial z}+\frac{3}{2}\rho(z_{1})\rho^{\prime}(z_{1})m\frac{\partial m}{ \partial q}.\frac{\partial q}{\partial z}=0\, \tag{282}\]  
\[Q_{2}:=\rho^{3}(z_{1})m(m^{2}-1)+\frac{1}{2}\rho^{\prime}(z_{1})\left[\rho^{ \prime}(z_{1})m+\rho(z_{1})\frac{\partial m}{\partial q}.\frac{\partial q}{ \partial z}\right]=0. \tag{283}\]  
Since the equation \(Q_{1}+iQ_{2}=0\) is fulfilled when the real and imaginary part are equal to zero, we have to set up \(Q_{1}=0\) and \(Q_{2}=0\). Also, we have assumed that the modulus parameter \(q\) of the elliptic integral in the Legendre form might depend on the complex coordinate \(z\), on which, by definition, depends the Weierstrass function \(\rho(z)\). However, in view of the complexity of the equations, for now, we shall consider the simplified case \(\frac{\partial q}{\partial z}=0\).
## 4 TSN for Industrial Communications
### TSN over Wireless  
Although time-sensitive networking is, in principle, independent of the MAC layer, all the TSN mechanisms detailed above were specified over the last few years assuming Ethernet as the MAC layer. Extending these mechanisms to the wireless domain is essential for smart manufacturing applications, and ongoing work aims to adapt TSN mechanisms to operate over a wireless medium. Emerging wireless technologies such as 5G and WiFi 7 are expected to revolutionize the industrial sector by untethering several industrial applications, making it more flexible, mobile, and reconfigurable at lower costs (installation and maintenance) [62]. Many of these applications demand deterministic latency and ultra-reliability, and hence TSN needs to be extended into the wireless domain since these applications/use cases will be implemented over a hybrid network (containing both wired and wireless devices), if not being completely wireless.  
The wireless nature of the communication medium introduces several challenges, including dynamic link quality, packet error rates (especially in harsh industrial environments), shared medium access, interference due to other wireless transmissions, unlicensed spectrum, and the mobility of end devices. In this section, we discuss how well TSN capabilities can be extended/integrated with 5G and WiFi 7, along with the ongoing efforts to meet the demands of the smart manufacturing industry using a hybrid wired-wireless TSN network.
## 3 Results and Discussion
### Effect of the Roller's Rotational Speed on Power Requirements at Different Feed Rates  
Figure 8 explains that the power requirement increased with the increase in both the roller's rotational speed and the feeding rate. This result is in line with the results in [28; 29]. With the roller rotational speeds increasing to 150 rpm, 200 rpm, 250 rpm and 300 rpm, the power requirement increased from 1.68 kW to 2.41 kW, 2.01 kW to 2.59 kW, 2.42 kW to 3 kW and 2.70 kW to 3.51 kW for feed rate levels of 600 kg/h, 700 kg/h, 800 kg/h and 900 kg/h, respectively. The increasing power requirements with the increase in both the roller's rotational speed and the feeding rate was due to the increased load on the electric motor.
## 2 The Hybrid Estimator  
We first review the salient details of the hybrid estimator as a means of providing background on the existing work and establishing context for our development of the EPSOM-Hyb estimator. As an initial step, the hybrid algorithm learns a dyadic partition of the parameter space that helps identify high-probability regions of the target distribution. In order to accomplish this, we form covariate-response pairs, \(\{\big{(}u_{j},\mathbb{F}(u_{j})\big{)}\}_{1\leq j\leq J}\), using MCMC samples from the target distribution \(\gamma\) and evaluating them with \(\mathbb{F}\). Clearly, we make the assumption of being able to sample from \(\gamma\) and evaluate \(\mathbb{F}\), but both of these are basic requirements in many MCMC-based algorithms. We can then use a regression tree algorithm to obtain optimal splits of the parameter space, where each of the partition sets is the hyper-rectangle that defines the corresponding leaf node in the fitted tree. The hybrid estimator uses the classification and regression tree (CART) algorithm [25], which returns a partition of the parameter space that can be further restricted to the compactification \(A\) of \(\mathcal{U}\) by setting \(A\) to be bounding box defined by the range of the posterior samples: \(A=\prod_{l=1}^{d}[\min_{j}\{u_{j}^{(l)}\},\max_{j}\{u_{j}^{(l)}\}]\), where \(u_{j}^{(l)}\) is the \(l\)-th coordinate of the \(j\)-th sample. Thus, the resulting partition \(\mathcal{A}\) is a dyadic partition of the compact set \(A\). We work with a compactification of the parameter space as a means of eliminating low-probability regions of the domain whose contributions to the integral in Equation (1) are negligible. This is particularly useful in Bayesian contexts where the posterior concentrates with increasing sample size [26; 27].  
Next, we formulate the piecewise estimator to \(\Psi\) defined over the compact set \(A\subseteq\mathcal{U}\),  
\[\Psi(u)\approx\widehat{\Psi}(u)=\sum_{k=1}^{K}\widehat{\Psi}_{k}(u)\mathbb{1} _{A_{k}}(u). \tag{2}\]  
where \(\mathcal{A}=\{A_{1},\ldots,A_{K}\}\), \(A=\bigcup_{k=1}^{K}A_{k}\), and \(A_{k}\cap A_{k^{\prime}}=\emptyset\) for all \(k\
eq k^{\prime}\). We set \(\widehat{\Psi}_{k}(u)=c_{k}^{\star}\), a representative point in \(A_{k}\) that is constant in \(u\). Since each \(A_{k}=\prod_{l=1}^{d}[a_{k}^{(l)},b_{k}^{(l)}]\) is a hyper-rectangle, the hybrid estimator reduces to  
\[\int_{A}e^{-\mathbb{F}(u)}\,du\approx\int_{A}e^{-\widehat{\Psi}(u)}\,du=\sum_{ k=1}^{K}e^{-c_{k}^{\star}}\cdot\mu(A_{k})=:\widehat{\mathcal{Z}}_{\text{Hyb}} \tag{3}\]  
Here, \(\mu(B)=\int_{B}1\,du\) denotes the \(d\)-dimensional volume of a set \(B\).
## 3 Problem Formulation  
Let V = {\(V_{1}\), \(V_{2}\), \(V_{3}\),..., \(V_{N}\)} represent the \(N\) number of reserved VMs with operating frequency (\(F_{1}\), \(F_{2}\), \(F_{3}\),..., \(F_{N}\)), cores (\(C_{1}\), \(C_{2}\), \(C_{3}\),..., \(C_{N}\)) and execution time (\(\tau_{1}\), \(\tau_{2}\),..., \(\tau_{N}\)). Each \(V_{i}\) where \(i\)\(\varepsilon\) [1, \(N\)] can be characterized as a triplet (\(F_{i}\), \(C_{i}\), \(\tau_{i}\)), thus \(F_{i}\) represents reserved frequency, and \(C_{i}\) represents the number of cores, \(\tau_{i}\) represents the execution time reserved for \(V_{i}\). Let \(S\) = {\(S_{1}\), \(S_{2}\), \(S_{3}\),..., \(S_{M}\)} represents \(M\) number of heterogeneous servers, each with \(k\) number of discrete frequencies (\(f_{0}\), \(f_{1}\), \(f_{2}\), \(f_{3}\),..., \(f_{k+1}\)) with utilization(\(U_{0}\), \(U_{1}\), \(U_{2}\),..., \(U_{k+1}\)), \(U_{0}\) = 0%,\(U_{k+1}\) = 100% and dynamic power (\(P_{0}\), \(P_{1}\), \(P_{2}\)\(P_{3}\),..., \(P_{k+1}\)). \(\mathrm{U_{0}}\) is idle state with power consumption \(P_{0}\). Each \(S_{j}\), where \(j\)\(\in\) [1,\(M\)] with utilization (\(U_{j,0}\), \(U_{j,1}\), \(U_{j,2}\),..., \(U_{j,k+1}\)) and power consumption (\(P_{j,0}\), \(P_{j,1}\), \(P_{j,2}\), \(P_{j,3}\),..., \(P_{j,k+1}\)) can be characterized as a triplet (\(U\)I\(t\)j, \(P_{j}\), \(T\)Cj), where \(U\)I\(t\)j is the current utilization of server \(S_{j}\), \(P_{j}\), is the power consumption of server \(S_{j}\), where as the \(T\)Cj is the total processing capacity of \(S_{j}\). The relation \(R\) between \(j\)th physical machine (PM) and \(i\)th VM indicates whether \(VM_{i}\) is placed in \(PM_{j}\), i.e.,  
\[\mathrm{R(j,i)}=\left\{\begin{array}{ll}1&\mathrm{VM_{i}^{CPU}\leq PM_{j}^{CPU }\ and\ \mathrm{VM_{i}^{mem}\leq PM_{j}^{mem},\mathrm{VM_{i}}is allocated to PM_{j}}}\\ &\mathrm{i\in[1,N]\ and\ j\in[1,M]}\\ 0&\mathrm{otherwise}\end{array}\right\} \tag{1}\]  
The Service Level Agreement (SLA) is measured using the Ratio of VM Acceptance (RVA) calculated as  
\[R\)V\(A(V)=T(R)/N \tag{2}\]  
where, \(N\) is the total number of VM requests submitted and \(T(R)\) is the total number of VM requests accepted and mapped to available PMs. It is derived as  
\[T(R)=\sum_{j=1}^{M}\sum_{i=1}^{N}R(j,i) \tag{3}\]
## 4 Experimental Results and Analysis
### Overall Decoding Performance  
We compare the performance results of our factorized parallelization, to the results obtained using a _ppefactor_ of 1 in Figure 16. It presents comparison between the performance exhibited both before and after the factorization of the PPE, with varying sizes of the coefficient matrixes, from 64 to 512. After identifying the optimal _ppefactor_, we obtain a speed increase of more than 1.5, using _PPE_COMPUTE_. On the other hand, _PPE_VECTOR_ and _PPE_TL_ exhibit negligible speed increases. These results are arranged and presented in Table 3. It is readily apparent that factorization is an important consideration when we decompose and rearrange tasks on heterogeneous multi-core processors.  
Figure 17 presents observed decoding times with coefficient matrices of varying sizes, from 64 to 512, when decoding different volumes of data, between 16 KB and 1 MB. We have shown above, in Section 4.2, that the parallelized Galois field multiplications using the SIMD instruction is the fastest implementation method on a homogeneous multi-core processor. In order to ensure a legitimate comparison with our implementations on the Cell BE, we implemented network coding on the Intel and AMD quad-core processors using only SIMD instructions. We have compared computing-based (_PPE_COMPUTE_), table-based (_PPE_TL_), and SIMD-based (_PPE_VECTOR_) multiplication methods on the PPE to the SPE using SIMD-based multiplication. In addition, the implementations of _PPE_COMPUTE_, _PPE_TL_, and _PPE_VECTOR_ exhibit average increases in speed of 0.32, 0.88, and 2.38, respectively, under experimental evaluation, as noted in Section 4.4. All implementations are compiled with the O3 level of the GNU GCC.  
Figure 16: Speed-up of the algorithms compared with the result of having factor “1” when varying coefficient matrix size; (**a**) 64 \(\times\) 64; (**b**) 128 \(\times\) 128; (**c**) 256 \(\times\) 256; and (**d**) 512 \(\times\) 512.  
In Figure 17, it can be seen that _PPE_COMPUTE_ demonstrates a low decoding speed when dealing with small data, however, it performs in a manner comparable to homogeneous processors as data size increases. This is because it incurs delay when the PPE is forced to wait for the SPEs during the decoding operation. In contrast, the other multiplication methods, which use table-based or parallelized SIMD-based multiplication on the PPE and parallelized SIMD-based multiplication on the SPE, on the Cell BE, exhibit fast decoding times in all experimental ranges. This gap increases with data size, as the gains from parallelization are enhanced.  
Figure 18 shows the average speed-ups varying the data size for all coefficient sizes; 64, 128, 256, and 512. It shows that the speed-ups are improved proportional to the data size; as the amount of computation increases, more data transmission to SPEs from main memory can be hidden. As we have shown in Figures 17 and 18, Cell BE is efficient for large data size
## Abbreviations  
\begin{tabular}{l l} \hline
**Symbol** & **Meaning** \\ \hline ACS & Active Cold Source \\ ADC & Analog-to-Digital Converter \\ AMP & AMPlifier \\ BP & Band Pass filer \\ CA & Calibration Assembly \\ ELBARA & ETH L-Band Radiometer for soil-moisture research. \\ ESA & European Space Agency \\ FC & Feed Cable \\ HS & Hot Source \\ IC & Instrument Controller \\ ISO & ISOlator \\ LP & Low Pass filer \\ MA & Microwave Assembly \\ ND & Noise Diode \\ PDA & Power Detector Assembly \\ PDU & Power Distribution Unit \\ PID & Proportional-Integral-Derivative controller \\ RFI & Radio Frequency Interferences \\ RM & RadioMeter \\ RS & Resistive Source \\ SMOS & Soil Moisture and Ocean Salinity mission \\ SW & input SWitch \\ TEC & Thermo-Electric Cooler \\ TPC & Temperature and Power Controller \\ USB / LSB & Upper / Lower Side Band \\ \hline \end{tabular}