esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Benchmark Results
### Hashing Algorithms  
We conducted benchmark tests for hash functions. A hash function is a one-way collision resistant function that compresses an arbitrarily large message into some information of fixed length. The construction of a hash function consists of two elements: a compression function that maps a fixed length input to a fixed length output, and a domain extender, that allows us to apply the compression function to inputs of variable sizes. Compression functions are usually designed using block ciphers. On the other hand, most hash functions have a domain extender that is constructed using an iterative structure, which is based on Merkle-Damgard hash construction. Since hash algorithms work in data blocks of fixed size, we assume temporal and energetic costs have a linear dependency with the input message size. Results confirm this assumption and also that the initialization phase of the algorithm is negligible.  
Figure 3 shows a comparison of the quantity of data that can be hashed in a second of time and with a Joule of energy. The tested hash functions are MD5, SHA-1 and SHA-2. MD5 and SHA-1 are the most widely used hash functions nowadays. However, they are vulnerable to some attacks ([8,9]) and National Institute of Standards and Technology (NIST) is recommending to replace them with the family of SHA-2 functions. From SHA-2 family, we have tested in particular SHA-256, SHA-384 and SHA-512.  
The faster device, Hx2790, clearly outperforms the other and, as in the symmetric cryptography case, the improvements are greater in the temporal domain than in the energetic one. The algorithms of the SHA-2 family are the ones which benefits more of a faster processor, with differences between Hx2790 and IPAQ 3970 greater than fivefold. Compared with symmetric algorithms results, the simpler hash functions (MD5, SHA-1) are at least two times faster, while the behaviour of SHA-2 functions is similar to the AES cryptosystems.  
For each device, the energy cost relation between all the algorithms is quite similar to what happened in temporal costs. However, the main difference is that MD5 and SHA-1 are more energy efficient in iPAQ3970 than in Hx2790. This is because these algorithms are simple and can be executed with the same number of instructions in both devices, and executing an instruction requires less energy in iPAQ3970 than in Hx2790.
## 2 Materials and Methods
### Network Scenarios  
Four network scenarios were selected for the deployed detectors. Tests were made during working hours and at the same time periods. The first scenario is a wireless network in a public segment. The second scenario is a wired network in a private environment, mostly with World Wide Web traffic. The third segment is a server farm with a secure configuration. The last scenario is a LAN in a militarized zone (MZ) with VoIP and streaming services.
# Efficient LDPC Encoder Design for IoT-Type Devices
## 3 LDPC Codes  
### Preliminaries  
A binary \((N,K)\) LDPC code is defined by its binary parity-check matrix \(\mathbf{H}\) of size \(M\times N\), where \(N\) is the code vector length, \(M=N-K\) is the number of parity-check bits, and \(K<N\) is the length of an information vector. The code redundancy is indicated by the code rate, which is defined as \(R=K/N\). In the systematic encoding process, \(M\) parity bits are added to the information vector \(\mathbf{u}=\{u_{1},u_{2},\ldots,u_{K}\}\), forming the code vector (code word) \(\mathbf{c}=\{c_{1},c_{2},\ldots,c_{N}\}\). In the mathematical formulation, the vectors are over the Galois field GF\((q)\)--specifically, GF\((2)\) for the binary codes considered in this paper.  
In the decoder, the received data in a row vector \(\mathbf{\hat{c}}\) of length \(N\) are recognized as a correct (error-free) vector if, and only if, they satisfy the parity-check equation \(\mathbf{H}\mathbf{\hat{c}}^{T}=\mathbf{0}_{M\times 1}\) with GF\((2)\) arithmetic. If the parity-check equation is not satisfied, error-correction decoding is required, which is applied by means of the iterative belief propagation algorithm [7].  
To be effectively and efficiently utilized in an IoT system, the LDPC code should usually satisfy requirements regarding the code rate \(R=K/N\), the code-word length \(N\), decent error correction performance, and the possibility of implementing an encoder using very limited resources. Our proposed encoding scheme is versatile; that is, a broad range of \(N\) and \(R\) can be engaged, and we will present the results for a number of parity-check matrices with different \(N\) and \(R\). Unlike the frequently considered advanced communication devices based on custom ICs and FPGAs, we consider an IoT device with highly limited resources: limited memory and only a single computation core (CPU).
## 3 Privacy Policies Multitier Approach
### Privacy Policies Formation  
Based on the architecture of the Privacy Controller Agent (PCA), presented at Section 2 above, when the PCA receives a user's request for an electronic service, two documents must be retrieved and compared against each other; (i) the user's privacy preferences; and (ii) the electronic service privacy policy. The first is submitted by user and stored at the preferences repository and the second is submitted by the service provider, specifically for each electronic service, and is stored at the policy repository. Taking into account the hierarchy scheme discussed in Section 3.1 and the identified proper subsets, the central government issues a generic privacy policy document that describes, in a broad level, how specific data types can be accessed, processed, and stored, based on the underlying legal and regulatory framework at level _(1)_. As we move down on the hierarchy, privacy policy documents are issued from specific ministerial departments, level _(2)_, and further down from specific electronic services, level _(k)_. At each one of these levels, an acknowledgement of previously-made acceptations, through reference, is included along to a more explicit expression of data access, process, storage, and retention period, if required. A schematic representation of privacy policy documents creation is depicted in Figure 2 below:  
Figure 2: Electronic Service Privacy Policy Formation.  
The number of references to previous levels may vary on each document depending on the provider of the electronic service and the structure of government. In every case, all documents should obligatorily contain a reference to the higher level _(1)_ since it comprises a representation of the underlying legal and regulatory framework. To ensure a seamless transition of privacy requirements to lower levels we define the following mandatory rules which accompany the proposed approach.  
**Rule 1:** Each one of the {_Level(k), Level (k-1),..., Level 3, Level 2_} privacy policy documents must comply to Level 1 document.  
_Privacy policy document compliance is defined as the absence of negation or generalization from the stipulations and clauses it imposes._  
**Rule 2:** Each one of the {_Level(k), Level (k-1),..., Level 3, Level 2_} privacy policy documents must include direct or indirect reference to _Level 1_ document.  
**Rule 3:** Each one of the {_Level(k), Level (k-1),..., Level 3, Level 2_} privacy policy documents can introduce new stipulations and clauses, provided that they contradict to the existing ones.  
**Rule 4:** Each one of the {_Level(k), Level (k-1),..., Level 3, Level 2_} privacy policy documents can only particularize stipulations and clauses imposed by higher level documents.  
_Particularization of stipulations (S) and clauses (C) is valid only if the introduced stipulation (S') or clause (C') are subsets of S or C respectively._  
\[S^{\prime}\subset S\mid C^{\prime}\subset C \tag{2}\]
## Materials and Methods
### Methods
#### 2.2.2 Scaffold Characterization  
*PCL and HA/*PCL nonwovens were characterized as follows: morphology and composition via scanning electron microscopy (SEM) equipped with energy dispersive X-ray spectroscopy (EDX), density via pycnometry and mechanical properties via INSTRON dynamometry. For morphological analysis, the samples were mounted on aluminum stumps and sputter-coated with gold (Sputter Coater Emitech K550, Quorum Technologies Ltd., West Sussex, UK) and then were observed using a JSM-520 (Jeol Ltd., Tokyo, Japan) at 15-20 KV and 1000\(\times\) magnification. EDX was used to detect HA particles in HA/*PCL nonwovens by investigating the presence of Ca and P elements. The scaffold density was calculated as the ratio between its mass, weighted with a digital scale and its volume, measured with a glass pycnometer using absolute ethanol as a non-solvent able to soak the sample, following best practice procedures. The scaffold mechanical properties were characterized following ASTM D 882-91, as from previous studies [24]. Briefly, a dog bone-shaped template was used to cut samples out from visible defect-free parts of the meshes. Their average thickness was calculated from five measurements taken by means of a micrometer (Vogel S.r.l., Leno (BS), Italy) in the load-bearing part of each sample. Tensile characterization was performed until break, at room temperature and ambient humidity, by testing five replicates at a constant extension rate of 2 mm/min. The elastic modulus was calculated as the slope of the resulting stress-strain curve in the linear region.
###### Abstract  
Real-time tool condition monitoring (TCM) is becoming more and more important to meet the increased requirement of reducing downtime and ensuring the machining quality of manufacturing systems. However, it is difficult to satisfy both robustness and effectiveness of pattern recognition for a TCM system without using an unsupervised strategy. In this paper, a clustering-based TCM system is proposed that can be used for different machining conditions such as variable cutting parameters, variable cutters, and even variable cutting methods. The solution is based on a significant statistical correlation between tool wear and the distribution of cutting force features, which is revealed through the clustering results obtained from a novel clustering method based on adjacent grids searching (CAGS). This statistical correlation is converted into tool wear status by using an empirical factor that is robust for variable cutting processes. The proposed TCM system is completely unsupervised as a training-free procedure is used in the monitoring process. To verify the effectiveness of the system, a series of experiments are conducted, such as whole life-cycle wear experiment under same milling condition, tool wear experiment under variable milling conditions and tool wear experiment under same turning condition. The prediction accuracy of our system for tool wear experiment under variable milling conditions is 100%, 75% and 75%, respectively. In contrast, BP neural network, Bayesian network and SVM are used for tool wear prediction under the same conditions. Experimental results show the superiority and effectiveness of our TCM system based on cluster density of CAGS over several state-of-the-art supervised methods.  
tool condition monitoring; clustering; unsupervised; milling; turning
## 4 Results
### Paleoenvironmental Characteristics  
#### 4.4.1 Paleoclimate  
The paleoclimate plays a significant role in shaping the sedimentary geochemical background, chemical weathering intensity, and terrigenous input fluxes. The Sr/Cu ratio in mudstone is widely used to infer paleoclimate conditions during sedimentation, with higher values indicating progressively more arid and hotter climates [83;84]. For example, a Sr/Cu ratio below 10 indicates a warm and humid climate, whereas a ratio above 10 indicates a dry and hot climate [85]. The Sr/Cu ratios in the Qigu Formation ranged from 6.31 to 13.91, with an average of 10.60. Most samples had values greater than 10, while only four samples fell below this threshold (Table 2; Figure 10A), indicating an arid environment with localized humid intervals. The variation in Rb/Sr ratios within sedimentary records reflects the climate-driven changes in the composition of weathered rock materials [86]. Higher Rb/Sr ratios are generally interpreted as indicative of humid climates, whereas lower ratios are typically associated with arid conditions. The Rb/Sr ratios in the Qigu Formation ranged from 0.22 to 0.52, with an average of 0.35 (Table 2, Figure 10A), further supporting the interpretation of an arid paleoclimate for the Qigu Formation.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline
**Well** & **Depth (m)** & **Sr** & **Ba** & **Rb** & **Cu** & **Co** & **B** & **Ni** & **V** & **Ga** & **Cr** & **U** & **Th** \\ \hline Y306 & 5705.30 & 174.39 & 460.33 & 69.97 & 14.66 & 15.64 & 88.00 & 43.77 & 109.84 & 26.56 & 62.03 & 1.99 & 6.30 \\ Y306 & 5
# Phase Compensation Technique for Effective Heat Focusing in Microwave Hyperthermia Systems
## 2 The Antenna Array System for Microwave Hyperthermia Treatment
### The Antenna for the Array Configuration  
Since this paper is a part of ongoing research related to our existing work, the antenna for microwave hyperthermia used in this study was a TSA, based on our previous study [25]. The designed TSA has a small aperture size for easier application, and has been optimized to effectively deliver EM energy into the breast. The design configuration of a single antenna is presented in Figure 1a. To verify the EM effects on the 2D plane, a cylindrical shaped artificial breast phantom that takes into account the breast's fatty component (\(\varepsilon_{\tau}\) = 5.14, \(\sigma\) = 0.137 S/m) was modelled as homogeneous medium [26]. The designed TSA was printed on an 0.8-mm-thick FR-4 substrate (\(\varepsilon_{\tau}\) = 4.3, tan\(\delta\) = 0.025) over an 80 mm \(\times\) 40 mm area, as shown in Figure 1b [25]. The meander line was designed beneath the tapered slot of the TSA to operate at 2.45 GHz for the microwave hyperthermia treatment. In Figure 2, the simulated and measured reflection coefficients are compared. The reflection coefficient \((S_{11})\) was measured under the condition presented in Figure 1a, where the designed antenna is in direct contact with the phantom. For the antenna measurement and thermal experiment, a gelatin-based phantom was fabricated, and the dielectric property of performed phantom is \(\varepsilon_{\tau}\) = 5.1 at 2.45 GHz [27].  
For effective electric field concentration in the female breast phantom, each antenna is arranged vertically. For these antennas' arrangement, the same linear polarization profile in each array antenna element is related to the effective hyperthermia treatment. In Figure 3, the E-field distribution at 2.45 GHz in the breast phantom, including in the tumor, is presented. A spherical breast tumor of radius \(r\) = 5 mm is positio  
Figure 1: (**a**) Simulation configuration of the single tapered slot antenna (TSA) and (**b**) geometry of this antenna for microwave hyperthermia [25].  
Figure 2: The simulated and measured reflection coefficients for the used TSA antenna.  
cylindrical phantom. As shown in this figure, the polarization of the single TSA antenna is well-formed along the z-axis. A full simulation was performed with FIT-based CST Studio Suite 2019 [28].
## 4 Results
### EMT Simulation Results
The other two cascaded controllers (PR \(\mathcal{H}_{\infty}\) and cascaded \(\mathcal{H}_{\infty}\)) somehow show improved voltage and harmonic contents, both with very similar figures for 11 th and 13 th voltage and current harmonic components. On the other hand, the full \(\mathcal{H}_{\infty}\) controller shows substantially reduced voltage and harmonic components. All the shown results show that voltage and current performances improve using controllers based on \(\mathcal{H}_{\infty}\) synthesis. Moreover, active and reactive power performance also improve, especially when using the single-voltage controller, as shown in the DR-based WPP results. To summarise, controllers optimised using \(\mathcal{H}_{\infty}\) design techniques show better performance than their root locus counterparts, not only for controllers with different orders but also for controllers with the same order (\(\mathcal{H}_{\infty}\)-tuned PR). Not surprisingly, the full \(\mathcal{H}_{\infty}\) control performs better than the other three controllers.
## 2 Materials and Methods
### Data-Driven System Identification
#### 2.4.2 Non-Paramateric FRF Model Computation  
The input and output data are measured and recorded over M multiple periods and broken into the corresponding set of sub-records. In case the measurement occurs under  
Figure 5: Individual steps of data-driven identification algorithm.  
steady-state conditions, there will be no leakage due to the transients. The sample mean can be computed from the DFT spectra of the plant input and output signals  
\[\overline{U}(\omega_{k})=\frac{1}{M}\sum_{l=1}^{M}U^{[l]}(\omega_{k}),\ \overline{Y}(\omega_{k})=\frac{1}{M}\sum_{l=1}^{M}Y^{[l]}(\omega_{k}), \tag{21}\]  
with the corresponding (co)variances given as  
\[\hat{\sigma}_{U}^{2}(\omega_{k}) = \frac{1}{M-1}\sum_{l=1}^{M}|U^{[l]}(\omega_{k})-\overline{U}( \omega_{k})|^{2}, \tag{22}\] \[\hat{\sigma}_{Y}^{2}(\omega_{k}) = \frac{1}{M-1}\sum_{l=1}^{M}|Y^{[l]}(\omega_{k})-\overline{Y}( \omega_{k})|^{2},\] (23) \[\hat{\sigma}_{YU}^{2}(\omega_{k}) = \frac{1}{M-1}\sum_{l=1}^{M}(Y^{[l]}(\omega_{k})-\overline{Y}( \omega_{k}))(U^{[l]}(\omega_{k})-\overline{U}(\omega_{k})). \tag{24}\]  
The FRF estimate is acquired from the division of the averaged output and input spectra  
\[\hat{P}=\frac{\overline{Y}}{\overline{U}}=\frac{Y_{0}+N_{Y}}{U_{0}+N_{U}}, \tag{25}\]  
where \(Y_{0},U_{0}\) denote the true spectra and the corresponding disturbance \(N_{Y},N_{U}\) introduced by the measurement errors. Bias and variance values of the resulting model can be obtained from the Taylor expansion of the previous equation under some assumptions about the input and output noise signals [2]  
\[b=E\{\hat{P}\}-P_{0}=-P_{0}exp(-M\frac{|U_{0}|^{2}}{\sigma_{U}^{2}})(1-\rho \frac{U_{0}/\sigma_{U}}{Y_{0}/\sigma_{Y}}), \tag{26}\]  
where \(\rho\) is the correlation between the input and output noises  
\[\rho=\frac{\sigma_{YU}^{2}}{\sigma_{Y}\sigma_{U}}, \tag{27}\]  
and the variance estimate is obtained as  
\[\sigma_{\hat{P}}^{2}=\frac{1}{M}|P_{0}|^{2}\Bigg{(}\frac{\sigma_{Y}^{2}}{|Y_{ 0}|^{2}}+\frac{\sigma_{U}^{2}}{|U_{0}|^{2}}-2 Re(\frac{\sigma_{YU}^{2}}{Y_{0} U_{0}})\Bigg{)}. \tag{28}\]  
It can be deduced that the relative bias gets reduced exponentially with the number of averaged periods and increasing SNR of the plant input. Careful preparation of the identification experiment by designing optimized wide-band signals and repeating a sufficient number of periods can be used to mitigate the bias effect. The same holds for the variance of the resulting FRF estimates. The variance expression can be used for the construction of the confidence intervals, which may serve in the subsequent phase of parametric modelling for the derivation of the model uncertainty. This information can be used for the robust controller design.
## 5 Discussion
By evaluating both the prognostic value as well as reporting out metrics such as sensitivity and PPV and utilizing error analysis, researchers can be surer of the impact of using these extracted clinical details in generating RWE. A final strength of this study is the ability to extract multiple ECOG PS scores for a patient over time and not just at time of diagnosis. This enables a better understanding of a patient's longitudinal journey over time, as well as improved completeness of ECOG PS in later lines of therapy, which may be important for some research studies focused on care provided towards the end of life. Given the relevance of ECOG PS as a variable in oncology research, and the challenges associated with its overall documentation in RWD sources, our work has several implications for investigators. ECOG PS is critical to define potential clinical trial eligibility for use-cases in which RWE is generated for contextualization of clinical trials results [60, 61]. ECOG PS incompleteness may impair the generation of adequate study samples and introduce bias. Therefore, maximizing availability of this variable would facilitate these contextual studies and would minimize any potential selection bias associated with reliance only on patients with structured ECOG PS. ECOG PS can be an important confounder in oncology studies, the application of this algorithm can increase analytic robustness, interpretability, and relevance by enabling multivariate analyses, increasing their statistical power, and by mitigating the need for imputation approaches altogether. Similar NLP algorithms could be deployed in practice, beyond analytic scenarios, to automate clinical trial eligibility screening processes or to enhance risk assessment to aid in clinical decision making. While understanding the generalizability to other tumor types warrants further research, we believe basing our characterization step for the variable and the deeper analyses on one solid tumor type aNSCLC is an important contribution to the field and, to our knowledge, is among the first of its kind. For the scope of the present study, aNSCLC provided a disease setting where the ECOG PS documentation is particularly useful (due to expected frequent treatment changes in a short disease course), and the investigation of its relevance has been thorough [36, 62, 63], providing an ample backdrop of scientific literature to ascertain the performance of our NLP-extracted variable. Furthermore, while completeness of ECOG PS is indeed improved using an NLP algorithm, many patients still do not have an explicitly documented ECOG PS score and ECOG PS 3-4 is infrequently recorded. Future research should focus on novel methods to infer functional status and improve our understanding of the health of patients missing ECOG PS scores in their records.
## 4 Performance Analysis
### Transaction Security Analysis
#### 4.2.3 Security of the Stealth Address  
For the calculation of the stealth address \(T=c\cdot\)G + \(B=Hash(r\cdot S)\cdot\)G + \(B\), where \(S\), \(B\), and G are public information, but for the temporary private key \(r=Random(seed)\) of the transaction, the randomness seed of its calculation comes from the common knowledge known only to the two parties of the transaction, and only the two parties of the transaction know the temporary private key \(r\) to calculate the same stealth address \(T_{r}\), and only the corresponding recipient has the confirmation key \(t\) for the stealth address and can prove ownership of it.
# Narrowband Internet of Things (NB-IoT):
## 2 Narrowband-IoT Standard and Releases
### Release 13
#### 2.1.3 Complexity and Cost Reduction Techniques  
NB-IoT is required to have low complexity to reach the low-cost objective to facilitate massive connections. The features that were implemented to reach this objective include relaxed base-band processing, low memory storage, and reduced radio-frequency (RF) components. In this regard, the system bandwidth is set as narrow as 180 kHz with reduced frequency and time synchronization requirement. Also, NB-IoT uses the restricted BPSK and QPSK modulation schemes with only one antenna support both in uplink and downlink transmission.